Lecture Notes in Computer Science 6691 · 2011. 7. 25. · Lecture Notes in Computer Science 6691 Commenced Publication in 1973 Founding and Former Series Editors: ... izations in

Lecture Notes in Computer Science 6691Commenced Publication in 1973Founding and Former Series Editors:Gerhard Goos, Juris Hartmanis, and Jan van Leeuwen

Editorial Board

David HutchisonLancaster University, UK

Takeo KanadeCarnegie Mellon University, Pittsburgh, PA, USA

Josef KittlerUniversity of Surrey, Guildford, UK

Jon M. KleinbergCornell University, Ithaca, NY, USA

Alfred KobsaUniversity of California, Irvine, CA, USA

Friedemann MatternETH Zurich, Switzerland

John C. MitchellStanford University, CA, USA

Moni NaorWeizmann Institute of Science, Rehovot, Israel

Oscar NierstraszUniversity of Bern, Switzerland

C. Pandu RanganIndian Institute of Technology, Madras, India

Bernhard SteffenTU Dortmund University, Germany

Madhu SudanMicrosoft Research, Cambridge, MA, USA

Demetri TerzopoulosUniversity of California, Los Angeles, CA, USA

Doug TygarUniversity of California, Berkeley, CA, USA

Gerhard WeikumMax Planck Institute for Informatics, Saarbruecken, Germany

Joan Cabestany Ignacio RojasGonzalo Joya (Eds.)

Advancesin ComputationalIntelligence

11th International Work-Conferenceon Artificial Neural Networks, IWANN 2011Torremolinos-Málaga, Spain, June 8-10, 2011Proceedings, Part I

13

Volume Editors

Joan CabestanyUniversitat Politècnica de Catalunya (UPC)Departament d’Enginyeria ElectrònicaCampus Nord, Edificio C4, c/ Gran Capità s/n, 08034 Barcelona, SpainE-mail: [email protected]

Ignacio RojasUniversity of GranadaDepartment of Computer Architecture and Computer TechnologyC/ Periodista Daniel Saucedo Aranda, 18071 Granada, SpainE-mail: [email protected]

Gonzalo JoyaUniversidad de Málaga, Departamento Tecnologia ElectrónicaCampus de Teatinos, 29071 Málaga, SpainE-mail: [email protected]

ISSN 0302-9743 e-ISSN 1611-3349ISBN 978-3-642-21500-1 e-ISBN 978-3-642-21501-8DOI 10.1007/978-3-642-21501-8Springer Heidelberg Dordrecht London New York

Library of Congress Control Number: 2011928243

CR Subject Classification (1998): J.3, I.2, I.5, C.2.4, H.3.4, D.1, D.2

LNCS Sublibrary: SL 1 – Theoretical Computer Science and General Issues

© Springer-Verlag Berlin Heidelberg 2011This work is subject to copyright. All rights are reserved, whether the whole or part of the material isconcerned, specifically the rights of translation, reprinting, re-use of illustrations, recitation, broadcasting,reproduction on microfilms or in any other way, and storage in data banks. Duplication of this publicationor parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965,in its current version, and permission for use must always be obtained from Springer. Violations are liableto prosecution under the German Copyright Law.The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply,even in the absence of a specific statement, that such names are exempt from the relevant protective lawsand regulations and therefore free for general use.

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Preface

We are proud to present the set of final accepted papers for the eleventh editionof the IWANN conference “International Work-Conference on Artificial NeuralNetworks” held in Torremolinos (Spain) during June 8–10, 2011.

IWANN is a biennial conference that seeks to provide a discussion forumfor scientists, engineers, educators and students about the latest ideas and real-izations in the foundations, theory, models and applications of hybrid systemsinspired by nature (neural networks, fuzzy logic and evolutionary systems) aswell as in emerging areas related to the above items. As in previous editionsof IWANN, this year’s event also aimed to create a friendly environment thatcould lead to the establishment of scientific collaborations and exchanges amongattendees. Since the first edition in Granada (LNCS 540, 1991), the conferencehas evolved and matured. The list of topics in the successive Call for Papers hasalso evolved, resulting in the following list for the present edition:

1. Mathematical and theoretical methods in computational intelli-gence: Mathematics for neural networks; RBF structures; Self-organizingnetworks and methods; Support vector machines and kernel methods; Fuzzylogic; Evolutionary and genetic algorithms

2. Neurocomputational formulations: Single-neuron modelling; Perceptualmodelling; System-level neural modelling; Spiking neurons; Models of bio-logical learning

3. Learning and adaptation: Adaptive systems; Imitation learning; Recon-figurable systems; Supervised, non-supervised, reinforcement and statisticalalgorithms

4. Emulation of cognitive functions: Decision making; Multi-agent systems;Sensor mesh; Natural language; Pattern recognition; Perceptual and motorfunctions (visual, auditory, tactile, virtual reality, etc.); Robotics; Planningmotor control

5. Bio-inspired systems and neuro-engineering: Embedded intelligent sys-tems; Evolvable computing; Evolving hardware; Microelectronics for neural,fuzzy and bioinspired systems; Neural prostheses; Retinomorphic systems;Brain–computer interfaces (BCI) nanosystems; Nanocognitive systems

6. Hybrid intelligent systems: Soft computing; Neuro-fuzzy systems; Neuro-evolutionary systems; Neuro-swarm; Hybridization with novel computingparadigms: Qantum computing, DNA computing, membrane computing;Neural dynamic logic and other methods; etc.

7. Applications: Image and signal processing; Ambient intelligence; Biomimeticapplications; System identification, process control, and manufacturing; Com-putational biology and bioinformatics; Internet modeling, communicationand networking; Intelligent systems in education; Human–robot interaction.Multi-agent systems; Time series analysis and prediction; Data mining andknowledge discovery

VI Preface

At the end of the submission process, we had 202 papers on the above topics.After a careful peer-review and evaluation process (each submission was reviewedby at least 2, and on average 2.4, Program Committee members or additionalreviewer), 154 papers were accepted for oral or poster presentation, accordingto the recommendations of reviewers and the authors’ preferences.

It is important to note that for the sake of consistency and readability ofthe book, the presented papers are not organized as they were presented in theIWANN 2011 sessions, but classified under 21 chapters and with one chapteron the associated satellite workshop. The organization of the papers is in twovolumes and arranged following the topics list included in the call for papers.The first volume (LNCS 6691), entitled Advances in Computational Intelligence.Part I is divided into ten main parts and includes the contributions on:

1. Mathematical and theoretical methods in computational intelligence2. Learning and adaptation3. Bio-inspired systems and neuro-engineering4. Hybrid intelligent systems5. Applications of computational intelligence6. New applications of brain–computer interfaces7. Optimization algorithms in graphic processing units8. Computing languages with bio-inspired devices and multi-agent systems9. Computational intelligence in multimedia processing

10. Biologically plausible spiking neural processing

In the second volume (LNCS 6692), with the same title as the previous vol-ume, we have included the contributions dealing with topics of IWANN andalso the contributions to the associated satellite workshop (ISCIF 2011). Thesecontributions are grouped into 11 chapters with one chapter on the satelliteworkshop:

1. Video and image processing2. Hybrid artificial neural networks: models, algorithms and data3. Advances in machine learning for bioinformatics and computational

biomedicine4. Biometric systems for human–machine interaction5. Data mining in biomedicine6. Bio-inspired combinatorial optimization7. Applying evolutionary computation and nature-inspired algorithms to for-

mal methods8. Recent advances on fuzzy logic and soft computing applications9. New advances in theory and applications of ICA-based algorithms

10. Biological and bio-inspired dynamical systems11. Interactive and cognitive environments12. International Workshop of Intelligent Systems for Context-Based Informa-

tion Fusion (ISCIF 2011)

Preface VII

During the present edition, the following associated satellite workshops wereorganized:

1. 4th International Conference on Computational Intelligence in Se-curity for Information Systems (CISIS 2011). CISIS aims to offer ameeting opportunity for academic and industry-related researchers belongingto the various vast communities of computational intelligence, informationsecurity, and data mining. The corresponding selected papers are publishedin an independent volume (LNCS 6694).

2. International Workshop of Intelligent Systems for Context-BasedInformation Fusion (ISCIF 2011). This workshop provides an interna-tional forum to present and discuss the latest scientific developments andtheir effective applications, to assess the impact of the approach, and to fa-cilitate technology transfer. The selected papers are published as a separatechapter in the second volume (LNCS 6692).

3. Third International Workshop on Ambient-Assisted Living(IWAAL). IWAAL promotes the collaboration among researchers in thisarea, concentrating efforts on the quality of life, safety and health problems ofelderly people at home. IWAAL papers are published in LNCS volume 6693.

The 11th edition of IWANN was organized by the Universidad de Malaga,Universidad de Granada and Universitat Politecnica de Catalunya, together withthe Spanish Chapter of the IEEE Computational Intelligence Society. We wishto thank to the Spanish Ministerio de Ciencia e Innovacion and the Universityof Malaga for their support and grants.

We would also like to express our gratitude to the members of the differentcommittees for their support, collaboration and good work. We specially thankthe organizers of the associated satellite workshops and special session organiz-ers. Finally, we want to thank Springer, and especially Alfred Hofmann, AnnaKramer and Erika Siebert-Cole, for their continuous support and cooperation.

June 2011 Joan CabestanyIgnacio RojasGonzalo Joya

Organization

IWANN 2011 Organizing Committee

Honorary Chairs

Alberto Prieto University of GranadaFrancisco Sandoval University of Malaga

Conference ChairsJoan Cabestany Polytechnic University of CataloniaIgnacio Rojas University of GranadaGonzalo Joya University of Malaga

Technical Program Chairs

Francisco Garcia University of MalagaMiguel Atencia University of Malaga

Satellite Worshops Chairs

Juan M. Corchado University of SalamancaJose Bravo University of Castilla la Mancha

Publicity and Publication Chairs

Pedro Castillo University of GranadaAlberto Guillen University of GranadaBeatriz Prieto University of Granada

IWANN 2011 Program Committee

Plamen Angelov University of LancasterCecilio Angulo Polytechnic University of CataloniaA. Artes Rodriguez University of Carlos III, MadridAntonio Bahamonde University of OviedoR. Babuska Delft University of TechnologySergi Bermejo Polytechnic University of CataloniaPiero P. Bonissone GE Global ResearchAndreu Catala Polytechnic University of CataloniaGert Cauwenberghs University of California, San DiegoJesus Cid-Sueiro University of Carlos III, MadridRafael Corchuelo University of Seville

X Organization

Óscar Cordón European Centre for Soft ComputingCarlos Cotta University of MalagaMarie Cottrell University of Paris IAlicia D’Anjou University of the Basque CountryLuiza De Macedo Mourelle State University of Rio de Janeiro (UERJ)Dante Del Corso Polytechnic of TurinAngel P. del Pobil University of Jaume I, CastellonRichard Duro University of A CoruñaMarcos Faundez-Zanuy Polytechnic University of MataroJ. Manuel Ferrández Polytechnic University of CartagenaKunihiko Fukushima Takatsuki, OsakaChistian Gamrat CEA, Gif sur YvettePatrik Garda University Paris Sud, OrsayF. Javier Gonzalez Cañete University of MalagaKarl Goser University of DortmundManuel Graña University of the Basque CountryAnne Guerin-Dugue Institut National Polytechnique

de GrenobleHani Hagras University of EssexAlister Hamilton University of EdinburghJeanny Hérault GIPSA-Lab, INPG, GrenobleLuis Javier Herrera University of GranadaFrancisco Herrera University of GranadaCesar Hervás University of CordobaTom Heskes Radboud University NijmegenPedro Isasi University of Carlos III, MadridSimon Jones University of LoughbouroughChristian Jutten GIPSA-lab/DIS - CNRS - Grenoble

UniversityKathryn Klemic Yale UniversityAmaury Lendasse Helsinki University of TechnologyKurosh Madani University of Paris XIIJordi Madrenas Polytechnic University of CataloniaLúıs Magdalena ECSC MieresDario Maravall Polytechnic University of MadridBonifacio Mart́ın Del Brio University of ZaragozaFrancesco Masulli University of La Spezia, GenoaJose M. Molina University of Carlos III, MadridAugusto Montisci University of CagliariClaudio Moraga European Centre for Soft ComputingJuan M. Moreno Polytechnic University of CataloniaKlaus-Robert Muller FIRST, BerlinJose Muñoz University of MalagaAlan F. Murray Edinburgh UniversityJean-Pierre Nadal Normal Superior School, Paris

Organization XI

Nadia Nedjah State University of Rio de JaneiroErkki Oja Helsinki University of TechnologyMadalina Olteanu University of Paris IJulio Ortega University of GranadaKevin M. Passino The Ohio State University USAWitold Pedrycz University of AlbertaFrancisco Pelayo University of GranadaVincenzo Piuri University of MilanHector Pomares University of GranadaCarlos G. Puntonet University of GranadaLeonardo Reyneri Polytechnic of TurinEduardo Ros University of GranadaUlrich Rueckert University of PaderbornEduardo Sanchez LSI, EPFLJordi Solé-Casals University of VicPeter Szolgay Pazmany Peter Catholic UniversityJohn Taylor Kings College London, UKCarme Torras Polytechnic University of CataloniaI. Burhan Turksen TOBB Econ Technol. University, AnkaraMark Van Rossum University of EdinburghMarley Vellasco Pontif. Catholic University of Rio

de JaneiroAlfredo Vellido Polytechnic University of CataloniaMichel Verleysen Catholic University of Louvain-la-NeuveThomas Villmann University of LeipzigChangjiu Zhou Singapore PolytechnicAhmed Zobaa University of CairoPedro Zufiria Polytechnic University of Madrid

IWANN 2011 Reviewers

Carlos Affonso Nove de Julho UniversityVanessa Aguiar University of A CoruñaArnulfo Alanis Garza Instituto Tecnologico de TijuanaAmparo Alonso-Betanzos University of A CoruñaJuan Antonio Alvarez University of SevilleJhon Edgar Amaya University of TachiraCésar Andrés Complutense University of MadridAnastassia Angelopoulou University of WestminsterPlamen Angelov Lancaster UniversityDavide Anguita University of GenoaCecilio Angulo Polytechnic University of CataloniaAngelo Arleo CNRS - University Pierre and Marie Curie

Paris VIManuel Atencia IIIA-CSICMiguel Atencia University of Malaga

XII Organization

Jorge Azorin University of AlicanteDavide Bacciu IMT Lucca School for Advanced StudiesAntonio Bahamonde University of Oviedo at Gijón, AsturiasHalima Bahi University of AnnabaJavier Bajo Pont. University of SalamancaJuan Pedro Bandera University of MalagaCristian Barrué Polytechnic University of CataloniaBruno Baruque University of BurgosDavid Becerra University of the West of ScotlandLluis A. Belanche-Munoz Polytechnic University of CataloniaSergi Bermejo Polytechnic University of CataloniaNicu Bizdoaca University of CraiovaJuan Botia University of MurciaJulio Bregáins University of A CoruñaGloria Bueno University of Castilla-La ManchaJoan Cabestany Polytechnic University of CataloniaInma P Cabrera University of MalagaTomasa Calvo University of AlcalaJose Luis Calvo-Rolle University of A CoruñaMariano Carbonero-Ruz ETEA - Cordoba UniversityCarlos Carrascosa GTI-IA DSIC Universidad Politecnica

de ValenciaLuis Castedo University of A CoruñaPedro Castillo University of GranadaAna Cavalli GET/INTMiguel Cazorla University of AlicanteRaymond Chiong Swinburne University of TechnologyJesus Cid-Sueiro University of MadridMáximo Cobos Universidad Politecnica de ValenciaValentina Colla Scuola Superiore S. AnnaFeijoo Colomine University of TachiraPablo Cordero University of MalagaÓscar Cordón European Centre for Soft ComputingFrancesco Corona TKKUlises Cortes Polytechnic University of CataloniaCarlos Cotta University of MalagaMarie Cottrell Universite Paris IMario Crespo-Ramos University of OviedoRaúl Cruz-Barbosa Universidad Tecnológica de la MixtecaManuel Cruz-Ramı́rez Departamento de Informática y Análisis

NuméricoErzsébet Csuhaj-Varjú Hungarian Academy of SciencesDaniela Danciu University of CraiovaAdriana Dapena University of A CoruñaAlberto De La Encina Universidad Complutense

Organization XIII

Luiza De Macedo Mourelle State University of Rio de Janeiro (UERJ)Suash Deb C.V. Raman College of EngineeringJosé Del Campo-Ávila University of MalagaAngel P. Del Pobil Jaume-I UniversityEnrique Dominguez University of MalagaJulian Dorado University of A CoruñaRichard Duro University of A CoruñaGregorio Dı́az University of Castilla-La ManchaMarta Dı́az Polytechnic University of CataloniaEmil Eirola Helsinki University of TechnologyPatrik Eklund Umea UniversityPablo Estevez University of ChileMarcos Faundez-Zanuy Escola Universitaria Politecnica de MataroCarlos Fernandez University of A CoruñaJ. Fernandez De Cañete University of MalagaAlberto Fernandez Gil University Rey Juan CarlosE. Fernandez-Blanco University of A CoruñaJ.C. Fernández Caballero University of CordobaM. Fernández Carmona University of MalagaF. Fernández De Vega University of ExtremaduraAntonio Fernández Leiva University of MalagaF. Fernández Navarro University of CordobaJ. Manuel Ferrández Universidad Politecnica de CartagenaAnibal R. Figueiras-Vidal Universidad Politecnica de MadridOscar Fontenla-Romero University of A CoruñaLeonardo Franco University of MalagaAna Freire University of A CoruñaRamón Fuentes Universidad Publica de NavarraColin Fyfe University of the west of scotlandJosé Gallardo University of MalagaJose Garcia Rodŕıguez University of AlicanteFrancisco Garcia-Lagos University of MalagaMaite Garcia-Sebastian University of the Basque CountryJuan Miguel Garćıa Universidad Politecnica de ValenciaPatricio Garćıa Báez University of La LagunaPablo Garćıa Sánchez University of GranadaMaribel Garćıa-Arenas University of GranadaEsther Garćıa-Garaluz University of MalagaPatrick Garda UPMC (France)Marcos Gestal University of A CoruñaPeter Gloesekotter University of Applied Sciences MünsterJuan Gomez University of MadridLuis González Abril University of SevilleJesús González Peñalver University of GranadaJuan Gorriz University of Granada

XIV Organization

Karl Goser University of DortmundBernard Gosselin Université de MonsJorge Gosálbez Universidad Politecnica de ValenciaManuel Grana University of the Basque CountryBertha Guijarro-Berdiñas University of A CoruñaNicolás Guil University of MalagaAlberto Guillen University of GranadaPedro Antonio Gutiérrez University of CordobaVanessa Gómez-Verdejo University of MadridAndrei Halanay Polytechnic University of BucharestAlister Hamilton University of EdinburghFrancisco Herrera University of GranadaÁlvaro Herrero University of BurgosCesar Hervás University of CordobaTom Heskes Radboud University NijmegenM. Hidalgo-Herrero Universidad ComplutenseRob Hierons Brunel UniversityWei-Chiang Hong School of Management, Da Yeh UniversityJeanny Hérault GIPSA-Lab, INPG, GrenobleJosé Jerez University of MalagaM.D. Jimenez-Lopez University of Rovira i VirgiliJ.L. Jiménez Laredo University of GranadaSimon Jones University of LoughbouroughGonzalo Joya University of MalagaVicente Julian GTI-IA DSIC UPVChristian Jutten GIPSA-lab/DIS - CNRS - Grenoble

UniversityJorma Laaksonen Helsinki University of TechnologyAlberto Labarga University of GranadaVincent Lemaire Orange LabsAmaury Lendasse HUTPaulo Lisboa Liverpool John Moores UniversityEzequiel Lopez University of MalagaRafael Luque University of MalagaOtoniel López Miguel Hernandez UniversityGuillermo López Campos Institute of Health “Carlos III”M.A. López Gordo University of GranadaKurosh Madani LISSI / Université PARIS XIIJordi Madrenas Polytechnic University of CataloniaLúıs Magdalena ECSC MieresEnric Xavier Martin Rull Polytechnic University of CataloniaLuis Mart́ı University of MadridMario Mart́ın Polytechnic University of CataloniaBonifacio Mart́ın Del Brio University of ZaragozaJosé Mart́ın Guerrero Universiy of Valencia

Organization XV

José Lúıs Mart́ınez University of Castilla-La ManchaF.J. Mart́ınez-Estudillo ETEAFrancesco Masulli University of GenovaMontserrat Mateos Pont. University of SalamancaJesús Medina-Moreno University of CadizMercedes Merayo Complutense University of MadridJuan J. Merelo University of GranadaGustavo J. Meschino National University of Mar del PlataJose M. Molina University of MadridCarlos Molinero Complutense University of MadridFederico Montesini-Pouzols HUTAugusto Montisci University of CagliariAntonio Mora University of GranadaAngel Mora Bonilla University of MalagaClaudio Moraga European Centre for Soft ComputingGin Moreno University of Castilla la ManchaJuan M. Moreno Polytechnic University of CataloniaJuan Moreno Garćıa University of Castilla-La ManchaJose Muñoz University of MalagaSusana Muñoz Hernández Technical University of MadridE. Mérida-Casermeiro University of MalagaNadia Nedjah State University of Rio de JaneiroPedro Nuñez University of ExtremaduraManuel Núñez UCMSalomon Oak California State Polytechnic UniversityManuel Ojeda-Aciego University of MalagaMadalina Olteanu SAMOS, Université Paris 1Jozef Oravec PF UPJSJulio Ortega University of GranadaA. Ortega De La Puente Autonomous University of MadridJuan Miguel Ortiz University of MalagaInma P. De Guzmán University of MalagaOsvaldo Pacheco Universidade de AveiroEsteban Palomo University of MalagaDiego Pardo Polytechnic University of CataloniaMiguel Angel Patricio University of de MadridFernando L. Pelayo University of Castilla-La ManchaFrancisco Pelayo University of GranadaVincenzo Piuri University of MilanHector Pomares University of GranadaAlberto Prieto University of GranadaMar Prueba University of MalagaAleka Psarrou University of WestminsterFrancisco Pujol University of AlicanteCarlos G. Puntonet University of Granada

XVI Organization

José Manuel Pérez University of JaenPablo Rabanal Complutense University of MadridJuan Rabuñal University of A CoruñaAnder Ramos University of TübingenDaniel Rivero University of A CoruñaIsmael Rodriguez Laguna Complutense University of MadridA. Rodriguez-Molinero Hospital Sant Antoni AbatJuan Antonio Rodŕıguez University of MalagaSara Rodŕıguez University of SalamancaDavid Rodŕıguez Rueda University of TachiraIgnacio Rojas University of GranadaFernando Rojas University of GranadaEnrique Romero Polytechnic University of CataloniaSamuel Romero Garcia University of GranadaRicardo Ron University of MalagaEduardo Ros University of GranadaFabrice Rossi TELECOM ParisTechPeter Roth Graz University of TechnologyLeonardo Rubio University of GranadaFernando Rubio Dı́ez Complutense University of MadridUlrich Rueckert University of PaderbornNicolás Ruiz Reyes University of JaenAmparo Ruiz Sepúlveda University of MalagaJoseph Rynkiewicz University of Paris IVladimir Râsvan University of CraiovaAddisson Salazar Universidad Politecnica de ValenciaSancho Salcedo-Sanz University of AlcaláAlbert Samà Polytechnic University of CataloniaMiguel A. Sanchez Pontifical University of SalamancaFrancisco Sandoval University of MalagaJose Santos University of A CoruñaJ.A. Seoane Fernández University of A CoruñaEduardo Serrano Autonomous University of MadridOlli Simula Helsinki University of TechnologyEvgeny Skvortsov Simon Fraser UniversitySergio Solinas Università degli studi di PaviaJordi Solé-Casals Universitat de VicAdrian Stoica Polytechnic University of BucharestJosé Luis Subirats University of MalagaPeter Szolgay Pazmany Peter Catholic UniversityJavier Sánchez-Monedero University of CordobaAna Maria Tomé Universidade de AveiroCarme Torras Polytechnic University of CataloniaClaude Touzet Université de ProvenceGracián Triviño University of Malaga

Organization XVII

Ricardo Téllez Pal RoboticsRaquel Ureña University of GranadaOlga Valenzuela University of GranadaGermano Vallesi Università Politecnica delle

Marche - AnconaAgust́ın Valverde University of MalagaPablo Varona Autonomous University of MadridM.A. Veganzones University of the Basque CountrySergio Velast́ın Kingston UniversityMarley Vellasco PUC-RioAlfredo Vellido Polytechnic University of CataloniaFrancisco Veredas University of MalagaMichel Verleysen Université catholique de LouvainBart Wyns Ghent UniversityVicente Zarzoso University of Nice Sophia AntipolisCarolina Zato University of SalamancaAhmed Zobaa University of Exeter

IWANN 2011 Invited Speakers

Hani Hagras The Computational Intelligence CentreSchool of Computer Science andElectronic Engineering, University of Essex,UK

Francisco Herrera Head of Research Group SCI2S(Soft Computing and Intelligent InformationSystems), Department of Computer Scienceand Artificial Intelligence,University of Granada, Spain

Tom Heskes Head of Machine Learning Group,Intelligent Systems Institute for Computingand Information Sciences (iCIS) Faculty ofScience Radboud University Nijmegen,The Netherlands

IWANN 2011 Special Sessions Organizers

New Applications of Brain–Computer Interfaces

Francisco Pelayo University of GranadaM.A. López Gordo University of GranadaRicardo Ron University of Malaga

XVIII Organization

Optimization Algorithms in Graphic Processing Units

Antonio Mora University of GranadaMaribel Garćıa-Arenas University of GranadaPedro Castillo University of Granada

Computing Languages with Bio-inspired Devices

M. D. Jimenez-Lopez University of Rovira i VirgiliA. Ortega De La Puente Autonomous University of Madrid

Computational Intelligence in Multimedia

Adriana Dapena University of A CoruñaJulio Bregáins University of A CoruñaNicolás Guil University of Malaga

Biologically Plausible Spiking Neural Processing

Eduardo Ros University of GranadaRichard R. Carrillo University of Almeria

Video and Image Processing

Enrique Domı́nguez University of MalagaJosé Garćıa University of Alicante

Hybrid Artificial Neural Networks: Models, Algorithms and Data

Cesar Hervás University of CordobaPedro Antonio Gutiérrez University of Crdoba

Advances in Machine Learning for Bioinformatics and ComputationalBiomedicinePaulo J.L. Lisboa Liverpool John Moores UniversityAlfredo Vellido Polytechnic University of CataloniaLeonardo Franco University of Malaga

Biometric Systems for Human–Machine Interaction

Alexandra Psarrou University of WestminsterAnastassia Angelopoulou University of WestminsterC.M. Travieso-Gonzlez University of Las Palmas de Gran CanariaJordi Solé-Casals University of Vic

Organization XIX

Data Mining in Biomedicine

Julián Dorado University of A CoruñaJuan R. Rabuñal University of A CoruñaAlejandro Pazos University of A Coruña

Bio-inspired Combinatorial Optimization

Carlos Cotta Porras University of MalagaAntonio J. Fernández Leiva University of Malaga

Applying Evolutionary Computation and Nature-InspiredAlgorithms to Formal Methods

Ismael Rodŕıguez Complutense University of Madrid

Recent Advances on Fuzzy Logic and Soft Computing Applications

Inma P. Cabrera University of MalagaPablo Cordero University of MalagaManuel Ojeda-Aciego University of Malaga

New Advances in Theory and Applications of ICA-Based Algorithms

Addison Salazar Polytechnic University of ValenciaLuis Vergara Polytechnic University of Valencia

Biological and Bio-inspired Dynamical Systems

Vladimir Rasvan University of CraiovaDaniela Danciu University of Craiova

Interactive and Cognitive Environments

Andreu Catalá Polytechnic University of CataloniaCecilio Angulo Polytechnic University of Catalonia

Table of Contents – Part I

Mathematical and Theoretical Methods inComputational Intelligence

Gaze Gesture Recognition with Hierarchical Temporal MemoryNetworks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

David Rozado, Francisco B. Rodriguez, and Pablo Varona

Feature Selection for Multi-label Classification Problems . . . . . . . . . . . . . . 9Gauthier Doquire and Michel Verleysen

A Novel Grouping Heuristic Algorithm for the Switch LocationProblem Based on a Hybrid Dual Harmony Search Technique . . . . . . . . . . 17

Sergio Gil-Lopez, Itziar Landa-Torres, Javier Del Ser,Sancho Salcedo-Sanz, Diana Manjarres, andJose A. Portilla-Figueras

Optimal Evolutionary Wind Turbine Placement in Wind FarmsConsidering New Models of Shape, Orography and Wind SpeedSimulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

B. Saavedra-Moreno, S. Salcedo-Sanz, A. Paniagua-Tineo,J. Gascón-Moreno, and J.A. Portilla-Figueras

Multi-Valued Neurons: Hebbian and Error-Correction Learning . . . . . . . . 33Igor Aizenberg

Multi-label Testing for CO2RBFN: A First Approach to the ProblemTransformation Methodology for Multi-label Classification . . . . . . . . . . . . 41

A.J. Rivera, F. Charte, M.D. Pérez-Godoy, and Maŕıa Jose del Jesus

Single Neuron Transient Activity Detection by Means ofTomography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

Carlos Aguirre, Pedro Pascual, Doris Campos, and Eduardo Serrano

Estimate of a Probability Density Function through Neural Networks . . . 57Leonardo Reyneri, Valentina Colla, and Marco Vannucci

Learning and Adaptation

A Neural Fuzzy Inference Based Adaptive Controller Using LearningProcess for Nonholonomic Robots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

Ting Wang, Fabien Gautero, Christophe Sabourin, andKurosh Madani

XXII Table of Contents – Part I

A Multi-objective Evolutionary Algorithm for Network IntrusionDetection Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

J. Gómez, C. Gil, R. Baños, A.L. Márquez, F.G. Montoya, andM.G. Montoya

A Cognitive Approach for Robots’ Vision Using Unsupervised Learningand Visual Saliency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81

Dominik M. Ramı́k, Christophe Sabourin, and Kurosh Madani

Fusing Heterogeneous Data Sources Considering a Set of EquivalenceConstraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89

Manuel Mart́ın-Merino

A Novel Heuristic for Building Reduced-Set SVMs Using theSelf-Organizing Map . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97

Ajalmar R. Rocha Neto and Guilherme A. Barreto

An Additive Decision Rules Classifier for Network IntrusionDetection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105

Tommaso Pani and Francisco de Toro

Multi-modal Opponent Behaviour Prognosis in E-Negotiations . . . . . . . . . 113Ioannis Papaioannou, Ioanna Roussaki, and Miltiades Anagnostou

Bio-inspired Systems and Neuro-engineering

An AER to CAN Bridge for Spike-Based Robot Control . . . . . . . . . . . . . . 124M. Dominguez-Morales, A. Jimenez-Fernandez, R. Paz,A. Linares-Barranco, D. Cascado, J.L. Coronado, J.L. Muñoz, andG. Jimenez

Neuromorphic Real-Time Objects Tracking Using Address EventRepresentation and Silicon Retina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133

F. Gómez- Rodŕıguez, L. Miró-Amarante, M. Rivas, G. Jimenez, andF. Diaz-del-Rio

Performance Study of Software AER-Based Convolutions on a ParallelSupercomputer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141

Rafael J. Montero-Gonzalez, Arturo Morgado-Estevez,Alejandro Linares-Barranco, Bernabe Linares-Barranco,Fernando Perez-Peña, Jose Antonio Perez-Carrasco, andAngel Jimenez-Fernandez

Frequency Analysis of a 64x64 Pixel Retinomorphic System with AEROutput to Estimate the Limits to Apply onto Specific MechanicalEnvironment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149

Fernando Perez-Peña, Arturo Morgado-Estevez,Alejandro Linares-Barranco, Gabriel Jimenez-Moreno,Jose Maria Rodriguez-Corral, and Rafael J. Montero-Gonzalez

Table of Contents – Part I XXIII

An AER Spike-Processing Filter Simulator and Automatic VHDLGenerator Based on Cellular Automata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157

Manuel Rivas-Perez, A. Linares-Barranco,Francisco Gomez-Rodriguez, A. Morgado, A. Civit, andG. Jimenez

A Biologically Inspired Neural Network for Autonomous UnderwaterVehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 166

Francisco Garćıa-Córdova and Antonio Guerrero-González

Hybrid Intelligent Systems

A Preliminary Study on the Use of Fuzzy Rough Set Based FeatureSelection for Improving Evolutionary Instance Selection Algorithms . . . . 174

Joaqúın Derrac, Chris Cornelis, Salvador Garćıa, andFrancisco Herrera

Forecasting Based on Short Time Series Using ANNs and Grey Theory– Some Basic Comparisons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183

Jelena Milojković, Vančo Litovski, Octavio Nieto-Taladriz, andSlobodan Bojanić

Short-Term Wind Power Forecast Based on Cluster Analysis andArtificial Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191

Javier Lorenzo, Juan Méndez, Modesto Castrillón, andDaniel Hernández

Back Propagation with Balanced MSE Cost Function and NearestNeighbor Editing for Handling Class Overlap and Class Imbalance . . . . . 199

R. Alejo, J.M. Sotoca, V. Garćıa, and R.M. Valdovinos

Combination of GA and ANN to High Accuracy of Polarimetric SARData Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 207

Ataollah Haddadi G. and Mahmodreza Sahebi

Gradient Descent Optimization for Routing in MultistageInterconnection Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215

Mehran Ghaziasgar and Armin Tavakoli Naeini

The Command Control of a Two-Degree-of-Freedom Platform by HandGesture Moment Invariants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223

Chih-Lyang Hwang and Chen-Han Yang

Network Intrusion Prevention by Using Hierarchical Self-OrganizingMaps and Probability-Based Labeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 232

Andres Ortiz, Julio Ortega, Antonio F. Dı́az, and Alberto Prieto

XXIV Table of Contents – Part I

Applications of Computational Intelligence

Human/Robot Interface for Voice Teleoperation of a RoboticPlatform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 240

L. Gallardo-Estrella and A. Poncela

Graph Laplacian for Semi-supervised Feature Selection in RegressionProblems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 248

Gauthier Doquire and Michel Verleysen

Detection of Transients in Steel Casting through Standard andAI-Based Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 256

Valentina Colla, Marco Vannucci, Nicola Matarese,Gerard Stephens, Marco Pianezzola, Izaskun Alonso,Torsten Lamp, Juan Palacios, and Siegfried Schiewe

Oesophageal Voice Harmonic to Noise Ratio Enhancement over UMTSNetworks Using Kalman-EM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 265

Marouen Azzouz, Begoña Garćıa Zapirain, Ibon Ruiz, andAmaia Méndez

Study of Various Neural Networks to Improve the Defuzzification ofFuzzy Clustering Algorithms for ROIs Detection in Lung CTs . . . . . . . . . 273

Alberto Rey, Alfonso Castro, and Bernardino Arcay

Differential Evolution Optimization of 3D Topological ActiveVolumes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 282

J. Novo, J. Santos, and M.G. Penedo

Genetic Algorithms Applied to the Design of 3D Photonic Crystals . . . . . 291Agust́ın Morgado-León, Alejandro Escúın, Elisa Guerrero,Andrés Yáñez, Pedro L. Galindo, and Lorenzo Sanchis

Sliding Empirical Mode Decomposition for On-line Analysis ofBiomedical Time Series . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 299

A. Zeiler, R. Faltermeier, A.M. Tomé, C. Puntonet,A. Brawanski, and E.W. Lang

Suitability of Artificial Neural Networks for Designing LoC Circuits . . . . 307David Moreno, Sandra Gómez, and Juan Castellanos

Aeration Control and Parameter Soft Estimation for a WastewaterTreatment Plant Using a Neurogenetic Design . . . . . . . . . . . . . . . . . . . . . . . 315

Javier Fernandez de Canete, Pablo del Saz-Orozco, andInmaculada Garcia-Moral

Table of Contents – Part I XXV

Pulse Component Modification Detection in Spino Cerebellar Ataxia 2Using ICA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323

Rodolfo V. Garćıa, Fernando Rojas, Jesús González, Luis Velázquez,Roberto Rodŕıguez, Roberto Becerra, and Olga Valenzuela

Early Pigmentary Retinosis Diagnostic Based on Classification Trees . . . 329Vivian Sistachs Vega, Gonzalo Joya Caparrós, andMiguel A. Dı́az Mart́ınez

New Applications of Brain-Computer Interfaces

Audio-Cued SMR Brain-Computer Interface to Drive a VirtualWheelchair . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 337

Francisco Velasco-Álvarez, Ricardo Ron-Angevin,Leandro da Silva-Sauer, Salvador Sancha-Ros, andMaŕıa José Blanca-Mena

A Domotic Control System Using Brain-Computer Interface (BCI) . . . . . 345Rebeca Corralejo, Roberto Hornero, and Daniel Álvarez

A Dictionary-Driven SSVEP Speller with a Modified Graphical UserInterface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 353

Ivan Volosyak, Anton Moor, and Axel Gräser

Non-invasive Brain-Computer Interfaces: Enhanced Gaming andRobotic Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 362

Reinhold Scherer, Elisabeth C.V. Friedrich, Brendan Allison,Markus Pröll, Mike Chung, Willy Cheung, Rajesh P.N. Rao, andChrista Neuper

An EEG-Based Design for the Online Detection of MovementIntention . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 370

Jaime Ibáñez, J. Ignacio Serrano, M. Dolores del Castillo,Luis Barrios, Juan Álvaro Gallego, and Eduardo Rocon

Auditory Brain-Computer Interfaces for Complete Locked-In Patients . . 378M.A. Lopez-Gordo, Ricardo Ron-Angevin, andFrancisco Pelayo Valle

Brain-Computer Interface: Generic Control Interface for SocialInteraction Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 386

C. Hintermüller, C. Guger, and G. Edlinger

Optimization Algorithms in Graphic Processing Units

Variable Selection in a GPU Cluster Using Delta Test . . . . . . . . . . . . . . . . 393A. Guillén, M. van Heeswijk, D. Sovilj, M.G. Arenas, L.J. Herrera,H. Pomares, and I. Rojas

XXVI Table of Contents – Part I

Towards ParadisEO-MO-GPU: A Framework for GPU-Based LocalSearch Metaheuristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 401

N. Melab, T.-V. Luong, K. Boufaras, and E.-G. Talbi

Efficient Simulation of Spatio–temporal Dynamics in UltrasonicResonators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 409

Pedro Alonso–Jordá, Jesús Peinado–Pinilla,Isabel Pérez–Arjona, and Victor J. Sánchez–Morcillo

GPU Implementation of a Bio-inspired Vision Model . . . . . . . . . . . . . . . . . 417Raquel Ureña, Christian Morillas, Samuel Romero, andFrancisco J. Pelayo

Bipartite Graph Matching on GPU over Complete or Local GridNeighborhoods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 425

Cristina Nader Vasconcelos and Bodo Rosenhahn

GPU Computation in Bioinspired Algorithms: A Review . . . . . . . . . . . . . . 433M.G. Arenas, A.M. Mora, G. Romero, and P.A. Castillo

Computing Languages with Bio-inspired Devices andMulti-Agent Systems

About Complete Obligatory Hybrid Networks of EvolutionaryProcessors without Substitution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 441

Artiom Alhazov, Gemma Bel-Enguix, Alexander Krassovitskiy, andYurii Rogozhin

Chemical Signaling as a Useful Metaphor for Resource Management . . . . 449Evgeny Skvortsov, Nima Kaviani, and Veronica Dahl

Distributed Simulation of P Systems by Means of Map-Reduce: FirstSteps with Hadoop and P-Lingua . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 457

L. Diez Dolinski, R. Núñez Hervás, M. Cruz Echeand́ıa, andA. Ortega

Hierarchy Results on Stateless Multicounter 5′ → 3′ Watson-CrickAutomata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 465

Benedek Nagy, László Hegedüs, and Ömer Eğecioğlu

Towards a Bio-computational Model of Natural Language Learning . . . . 473Leonor Becerra-Bonache

Computing Languages with Bio-inspired Devices and Multi-AgentSystems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 481

M. Dolores Jiménez-López

Table of Contents – Part I XXVII

Computational Intelligence in Multimedia Processing

A Novel Strategy for Improving the Quality of Embedded ZerotreeWavelet Images Transmitted over Alamouti Coding Systems . . . . . . . . . . . 489

Josmary Labrador, Paula M. Castro, Héctor J. Pérez–Iglesias, andAdriana Dapena

Applying Data Mining Techniques in a Wyner-Ziv to H.264 VideoTranscoder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 497

José Luis Mart́ınez, Alberto Corrales-Garćıa, Pedro Cuenca, andFrancisco José Quiles

On the Use of Genetic Algorithms to Improve Wavelet Sign CodingPerformance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 505

Ricardo Garćıa, Otoniel López, Antonio Mart́ı, andManuel P. Malumbres

Kernel-Based Object Tracking Using a Simple Fuzzy ColorHistogram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 513

Juan Villalba Espinosa, José Maŕıa González Linares,Julián Ramos Cózar, and Nicolás Guil Mata

Computational Intelligence in Multimedia Processing . . . . . . . . . . . . . . . . . 520Nicolás Guil, Julio C. Bregáins, and Adriana Dapena

Biologically Plausible Spiking Neural Processing

Isometric Coding of Spiking Haptic Signals by Peripheral SomatosensoryNeurons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 528

Romain Brasselet, Roland S. Johansson, and Angelo Arleo

Context Separability Mediated by the Granular Layer in a SpikingCerebellum Model for Robot Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 537

Niceto R. Luque, Jesús A. Garrido, Richard R. Carrillo, andEduardo Ros

Realistic Modeling of Large-Scale Networks: Spatio-temporal Dynamicsand Long-Term Synaptic Plasticity in the Cerebellum . . . . . . . . . . . . . . . . 547

Egidio D’Angelo and Sergio Solinas

Event and Time Driven Hybrid Simulation of Spiking NeuralNetworks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 554

Jesus A. Garrido, Richard R. Carrillo, Niceto R. Luque, andEduardo Ros

Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 563

Table of Contents – Part II

Video and Image Processing

Lossy Image Compression Using a GHSOM . . . . . . . . . . . . . . . . . . . . . . . . . 1E.J. Palomo, E. Domı́nguez, R.M. Luque, and J. Muñoz

Visual Features Extraction Based Egomotion Calculation from aInfrared Time-of-Flight Camera . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

Diego Viejo, Jose Garcia, and Miguel Cazorla

Feature Weighting in Competitive Learning for Multiple ObjectTracking in Video Sequences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

R.M. Luque, J.M. Ortiz-de-Lazcano-Lobato, Ezequiel López-Rubio,E. Domı́nguez, and E.J. Palomo

The Segmentation of Different Skin Colors Using the Combination ofGraph Cuts and Probability Neural Network . . . . . . . . . . . . . . . . . . . . . . . . 25

Chih-Lyang Hwang and Kai-Di Lu

Reduction of JPEG Compression Artifacts by Kernel Regression andProbabilistic Self-Organizing Maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

Maŕıa Nieves Florent́ın-Núñez, Ezequiel López-Rubio, andFrancisco Javier López-Rubio

An Unsupervised Method for Active Region Extraction in SportsVideos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

Markos Mentzelopoulos, Alexandra Psarrou, andAnastassia Angelopoulou

6DoF Egomotion Computing Using 3D GNG-Based Reconstruction . . . . 50Diego Viejo, Jose Garcia, and Miguel Cazorla

Fast Image Representation with GPU-Based Growing Neural Gas . . . . . . 58José Garćıa-Rodŕıguez, Anastassia Angelopoulou, Vicente Morell,Sergio Orts, Alexandra Psarrou, and Juan Manuel Garćıa-Chamizo

Texture and Color Analysis for the Automatic Classification of the EyeLipid Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66

L. Ramos, M. Penas, B. Remeseiro, A. Mosquera, N. Barreira, andE. Yebra-Pimentel

Quantitative Study and Monitoring of the Growth of Lung CancerNodule Using an X-Ray Computed Tomography Image ProcessingTool . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

José Luis Garćıa Arroyo, Begoña Garćıa Zapirain, andAmaia Méndez Zorrilla

XXX Table of Contents – Part II

A Geometrical Method of Diffuse and Specular Image ComponentsSeparation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83

Ramón Moreno, Manuel Graña, and Alicia d’Anjou

Optical Flow Reliability Model Approximated with RBF . . . . . . . . . . . . . . 90Agis Rodrigo, Dı́az Javier, Ortigosa Pilar, Guzmán Pablo, andRos Eduardo

Video and Image Processing with Self-organizing Neural Networks . . . . . 98José Garćıa-Rodŕıguez, Enrique Domı́nguez,Anastassia Angelopoulou, Alexandra Psarrou,Francisco José Mora-Gimeno, Sergio Orts, andJuan Manuel Garćıa-Chamizo

Hybrid Artificial Neural Networks: Models,Algorithms and Data

Parallelism in Binary Hopfield Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105José Muñoz-Pérez, Amparo Ruiz-Sepúlveda, andRafaela Beńıtez-Rochel

Multi-parametric Gaussian Kernel Function Optimization for �-SVMrUsing a Genetic Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113

J. Gascón-Moreno, E.G. Ortiz-Garćıa, S. Salcedo-Sanz,A. Paniagua-Tineo, B. Saavedra-Moreno, and J.A. Portilla-Figueras

Face Recognition System in a Dynamical Environment . . . . . . . . . . . . . . . . 121Aldo Franco Dragoni, Germano Vallesi, and Paola Baldassarri

Memetic Pareto Differential Evolutionary Neural Network forDonor-Recipient Matching in Liver Transplantation . . . . . . . . . . . . . . . . . . 129

M. Cruz-Ramı́rez, C. Hervás-Mart́ınez, P.A. Gutiérrez,J. Briceño, and M. de la Mata

Studying the Hybridization of Artificial Neural Networks in HECIC . . . . 137José del Campo-Ávila, Gonzalo Ramos-Jiménez,Jesús Pérez-Garćıa, and Rafael Morales-Bueno

Processing Acyclic Data Structures Using Modified Self-OrganizingMaps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145

Gabriela Andrejková and Jozef Oravec

Table of Contents – Part II XXXI

On the Performance of the μ-GA Extreme Learning Machines inRegression Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153

A. Paniagua-Tineo, S. Salcedo-Sanz, E.G. Ortiz-Garćıa,J. Gascón-Moreno, B. Saavedra-Moreno, and J.A. Portilla-Figueras

A Hybrid Evolutionary Approach to Obtain Better QualityClassifiers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161

David Becerra-Alonso, Mariano Carbonero-Ruz,Francisco José Mart́ınez-Estudillo, andAlfonso Carlos Mart́ınez-Estudillo

Neural Network Ensembles with Missing Data Processing and DataFusion Capacities: Applications in Medicine and in the Environment . . . 169

Patricio Garćıa Báez, Carmen Paz Suárez Araujo, andPablo Fernández López

Hybrid Artificial Neural Networks: Models, Algorithms and Data . . . . . . 177P.A. Gutiérrez and C. Hervás-Mart́ınez

Advances in Machine Learning for Bioinformaticsand Computational Biomedicine

Automatic Recognition of Daily Living Activities Based on aHierarchical Classifier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185

Oresti Banos, Miguel Damas, Hector Pomares, and Ignacio Rojas

Prediction of Functional Associations between Proteins by Means of aCost-Sensitive Artificial Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . 194

J.P. Florido, H. Pomares, I. Rojas, J.M. Urquiza, and F. Ortuño

Hybrid (Generalization-Correlation) Method for Feature Selection inHigh Dimensional DNA Microarray Prediction Problems . . . . . . . . . . . . . . 202

Yasel Couce, Leonardo Franco, Daniel Urda, José L. Subirats, andJosé M. Jerez

Model Selection with PLANN-CR-ARD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 210Corneliu T.C. Arsene, Paulo J. Lisboa, and Elia Biganzoli

Biometric Systems for Human-Machine Interaction

Gender Recognition Using PCA and DCT of Face Images . . . . . . . . . . . . . 220Ondrej Smirg, Jan Mikulka, Marcos Faundez-Zanuy,Marco Grassi, and Jiri Mekyska

Efficient Face Recognition Fusing Dynamic Morphological QuotientImage with Local Binary Pattern . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 228

Hong Pan, Siyu Xia, Lizuo Jin, and Liangzheng Xia

XXXII Table of Contents – Part II

A Growing Neural Gas Algorithm with Applications in Hand Modellingand Tracking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 236

Anastassia Angelopoulou, Alexandra Psarrou, andJosé Garćıa Rodŕıguez

Object Representation with Self-Organising Networks . . . . . . . . . . . . . . . . 244Anastassia Angelopoulou, Alexandra Psarrou, andJosé Garćıa Rodŕıguez

Data Mining in Biomedicine

SNP-Schizo: A Web Tool for Schizophrenia SNP SequenceClassification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 252

Vanessa Aguiar-Pulido, José A. Seoane, Cristian R. Munteanu, andAlejandro Pazos

MicroRNA Microarray Data Analysis in Colon Cancer: Effects ofNormalization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 260

Guillermo H. López-Campos, Alejandro Romera-López,Fernando Mart́ın-Sánchez, Eduardo Diaz-Rubio,Victoria López-Alomso, and Beatriz Pérez-Villamil

Automatic Handling of Tissue Microarray Cores in High-DimensionalMicroscopy Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 268

G. Bueno, M. Fernández, O. Déniz, and M. Garćıa-Rojo

Visual Mining of Epidemic Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 276Stéphan Clémençon, Hector De Arazoza, Fabrice Rossi, andViet-Chi Tran

Bio-inspired Combinatorial Optimization

Towards User-Centric Memetic Algorithms: Experiences with theTSP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 284

Ana Reyes Badillo, Carlos Cotta, and Antonio J. Fernández-Leiva

A Multi-objective Approach for the 2D Guillotine Cutting StockProblem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 292

Jesica de Armas, Gara Miranda, and Coromoto León

Ant Colony Optimization for Water Distribution Network Design:A Comparative Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 300

C. Gil, R. Baños, J. Ortega, A.L. Márquez, A. Fernández, andM.G. Montoya

A Preliminary Analysis and Simulation of Load Balancing TechniquesApplied to Parallel Genetic Programming . . . . . . . . . . . . . . . . . . . . . . . . . . . 308

F. Fernández de Vega, J.G. Abengózar Sánchez, and C. Cotta

Table of Contents – Part II XXXIII

A Study of Parallel Approaches in MOACOs for Solving the BicriteriaTSP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 316

A.M. Mora, J.J. Merelo, P.A. Castillo, M.G. Arenas, P.Garćıa-Sánchez, J.L.J. Laredo, and G. Romero

Optimizing Strategy Parameters in a Game Bot . . . . . . . . . . . . . . . . . . . . . 325A. Fernández-Ares, A.M. Mora, J.J. Merelo, P. Garćıa-Sánchez, andC.M. Fernandes

Implementation Matters: Programming Best Practices for EvolutionaryAlgorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 333

J.J. Merelo, G. Romero, M.G. Arenas, P.A. Castillo,A.M. Mora, and J.L.J. Laredo

Online vs Offline ANOVA Use on Evolutionary Algorithms . . . . . . . . . . . . 341G. Romero, M.G. Arenas, P.A. Castillo, J.J. Merelo, and A.M. Mora

Bio-inspired Combinatorial Optimization: Notes on Reactive andProactive Interaction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 348

Carlos Cotta and Antonio J. Fernández-Leiva

Applying Evolutionary Computation andNature-inspired Algorithms to Formal Methods

A Preliminary General Testing Method Based on Genetic Algorithms . . . 356Luis M. Alonso, Pablo Rabanal, and Ismael Rodŕıguez

Tackling the Static RWA Problem by Using a Multiobjective ArtificialBee Colony Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 364

Álvaro Rubio-Largo, Miguel A. Vega-Rodŕıguez,Juan A. Gómez-Pulido, and Juan M. Sánchez-Pérez

Applying a Multiobjective Gravitational Search Algorithm (MO-GSA)to Discover Motifs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 372

David L. González- Álvarez, Miguel A. Vega-Rodŕıguez,Juan A. Gómez-Pulido, and Juan M. Sánchez-Pérez

Looking for a Cheaper ROSA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 380Fernando L. Pelayo, Fernando Cuartero, and Diego Cazorla

A Parallel Skeleton for Genetic Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . 388Alberto de la Encina, Mercedes Hidalgo-Herrero,Pablo Rabanal, and Fernando Rubio

A Case Study on the Use of Genetic Algorithms to Generate Test Casesfor Temporal Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 396

Karnig Derderian, Mercedes G. Merayo, Robert M. Hierons, andManuel Núñez

XXXIV Table of Contents – Part II

Experimental Comparison of Different Techniques to GenerateAdaptive Sequences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 404

Carlos Molinero, Manuel Núñez, and Robert M. Hierons

Recent Advances on Fuzzy Logic and Soft ComputingApplications

An Efficient Algorithm for Reasoning about Fuzzy FunctionalDependencies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 412

P. Cordero, M. Enciso, A. Mora, I. Pérez de Guzmán, andJ.M. Rodŕıguez-Jiménez

A Sound Semantics for a Similarity-Based Logic ProgrammingLanguage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 421

Pascual Julián-Iranzo and Clemente Rubio-Manzano

A Static Preprocess for Improving Fuzzy Thresholded Tabulation . . . . . . 429P. Julián, J. Medina, P.J. Morcillo, G. Moreno, andM. Ojeda-Aciego

Non-deterministic Algebraic Structures for Soft Computing . . . . . . . . . . . . 437I.P. Cabrera, P. Cordero, and M. Ojeda-Aciego

Fuzzy Computed Answers Collecting Proof Information . . . . . . . . . . . . . . . 445Pedro J. Morcillo, Ginés Moreno, Jaime Penabad, andCarlos Vázquez

Implication Triples Versus Adjoint Triples . . . . . . . . . . . . . . . . . . . . . . . . . . . 453Ma Eugenia Cornejo, Jesús Medina, and Eloisa Ramı́rez

Confidence-Based Reasoning with Local Temporal Formal Contexts . . . . 461Gonzalo A. Aranda-Corral, Joaqúın Borrego Dı́az, andJuan Galán Páez

New Advances in Theory and Applications ofICA-Based Algorithms

Application of Independent Component Analysis for Evaluation ofAshlar Masonry Walls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 469

Addisson Salazar, Gonzalo Safont, and Luis Vergara

Fast Independent Component Analysis Using a New Property . . . . . . . . . 477Rubén Mart́ın-Clemente, Susana Hornillo-Mellado, andJosé Luis Camargo-Olivares

Using Particle Swarm Optimization for Minimizing Mutual Informationin Independent Component Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 484

Jorge Igual, Jehad Ababneh, Raul Llinares, and Carmen Igual

Table of Contents – Part II XXXV

Regularized Active Set Least Squares Algorithm for NonnegativeMatrix Factorization in Application to Raman Spectra Separation . . . . . . 492

Rafal Zdunek

A Decision-Aided Strategy for Enhancing Transmissions in WirelessOSTBC-Based Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 500

Tiago M. Fernández-Caramés, Adriana Dapena,José A. Garćıa-Naya, and Miguel González-López

Nonlinear Prediction Based on Independent Component AnalysisMixture Modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 508

Gonzalo Safont, Addisson Salazar, and Luis Vergara

Biological and Bio-inspired Dynamical Systems

Robustness of the “Hopfield Estimator” for Identification of DynamicalSystems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 516

Miguel Atencia, Gonzalo Joya, and Francisco Sandoval

Modeling Detection of HIV in Cuba . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 524Héctor de Arazoza, Rachid Lounes, Andres Sánchez,Jorge Barrios, and Ying-Hen Hsieh

Flexible Entrainment in a Bio-inspired Modular Oscillator for ModularRobot Locomotion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 532

Fernando Herrero-Carrón, Francisco B. Rodŕıguez, andPablo Varona

Dengue Model Described by Differential Inclusions . . . . . . . . . . . . . . . . . . . 540Jorge Barrios, Alain Piétrus, Aymée Marrero,Héctor de Arazoza, and Gonzalo Joya

Simulating Building Blocks for Spikes Signals Processing . . . . . . . . . . . . . . 548A. Jimenez-Fernandez, M. Domı́nguez-Morales,E. Cerezuela-Escudero, R. Paz-Vicente, A. Linares-Barranco, andG. Jimenez

Description of a Fault Tolerance System Implemented in a HardwareArchitecture with Self-adaptive Capabilities . . . . . . . . . . . . . . . . . . . . . . . . . 557

Javier Soto, Juan Manuel Moreno, and Joan Cabestany

Systems with Slope Restricted Nonlinearities and Neural NetworksDynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 565

Daniela Danciu and Vladimir Răsvan

Bio-inspired Systems. Several Equilibria. Qualitative Behavior . . . . . . . . . 573Daniela Danciu

XXXVI Table of Contents – Part II

Interactive and Cognitive Environments

Biologically Inspired Path Execution Using SURF Flow in RobotNavigation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 581

Xavier Perez-Sala, Cecilio Angulo, and Sergio Escalera

Equilibrium-Driven Adaptive Behavior Design . . . . . . . . . . . . . . . . . . . . . . . 589Paul Olivier and Juan Manuel Moreno Arostegui

Gait Identification by Using Spectrum Analysis on State SpaceReconstruction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 597

Albert Samà, Francisco J. Ruiz, Carlos Pérez, and Andreu Català

Aibo JukeBox A Robot Dance Interactive Experience . . . . . . . . . . . . . . . . . 605Cecilio Angulo, Joan Comas, and Diego Pardo

International Workshop of Intelligent Systems forContext-Based Information Fusion (ISCIF’11)

On Planning in Multi-agent Environment: Algorithm of SceneReasoning from Incomplete Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 613

Tomasz Grzejszczak and Adam Galuszka

Research Opportunities in Contextualized Fusion Systems. The HarborSurveillance Case . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 621

Jesus Garcia, José M. Molina, Tarunraj Singh, John Crassidis, andJames Llinas

Multiagent-Based Middleware for the Agents’ Behavior Simulation . . . . . 629Elena Garćıa, Sara Rodŕıguez, Juan F. De Paz, andJuan M. Corchado

A Dynamic Context-Aware Architecture for Ambient Intelligence . . . . . . 637José M. Fernández, Rubén Fuentes-Fernández, and Juan Pavón

Group Behavior Recognition in Context-Aware Systems . . . . . . . . . . . . . . . 645Alberto Pozo, Jesús Graćıa, Miguel A. Patricio, and José M. Molina

Context-Awareness at the Service of Sensor Fusion Systems: Invertingthe Usual Scheme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 653

Enrique Mart́ı, Jesús Garćıa, and Jose Manuel Molina

Improving a Telemonitoring System Based on Heterogeneous SensorNetworks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 661

Ricardo S. Alonso, Dante I. Tapia, Javier Bajo, and Sara Rodŕıguez

Table of Contents – Part II XXXVII

Supporting System for Detecting Pathologies . . . . . . . . . . . . . . . . . . . . . . . . 669Carolina Zato, Juan F. De Paz, Fernando de la Prieta, andBeatriz Mart́ın

An Ontological Approach for Context-Aware Reminders in AssistedLiving Behavior Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 677

Shumei Zhang, Paul McCullagh, Chris Nugent, Huiru Zheng, andNorman Black

Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 685

On the Use of Genetic Algorithms to Improve

Wavelet Sign Coding Performance

Ricardo Garćıa1,�, Otoniel López1, Antonio Mart́ı2, and Manuel P. Malumbres1

1 Miguel Hernández University,Avda. Universidad s/n, 03202, Elche, Spain

{r.garcia,otoniel,mels}@umh.es2 Universidad Politécnica de Valencia,

Camino de Vera s/n, 46222, Valencia, [email protected]

Abstract. Compression of wavelet coefficient sign has been assumed tobe inefficient for a long time. However, in the last years several proposalshave been developed and, in fact several image encoders like JPEG 2000include sign coding capabilities. In this paper, we present a new signcoding approximation using a genetic algorithm in order to efficientlypredict the sign of wavelet coefficients. We have included that predic-tion in a fast non-embedded image encoder. Preliminary results showthat, by including sign coding capabilities to a non-embedded encoder,the compression gain is up to 17.35%, being the Rate-Distortion (R/D)performance improvement up to 0.25 dB.

Keywords: sign coding, wavelets, image coding, genetic algorithms.

1 Introduction

Wavelet transforms have proved to be very powerful tools for image compres-sion. Many state-of-the-art image codecs, including the JPEG2000 standard [1],employ a wavelet transform in their algorithms. One advantage is the provisionof both frequency and spatial localization of image energy. The image energy iscompacted into a small fraction of the transform coefficients and compressioncan be achieved by coding these coefficients. The energy of a wavelet transformcoefficient is restricted to non-negative real numbers, but the coefficients them-selves are not, and they are defined by both a magnitude and a sign. Shapirostated in [2] that a transform coefficient is equally likely to be positive or nega-tive and thus one bit should be used to encode the sign. In recent years, severalauthors have begun to use context modeling for sign coding [3][4][5].

In [5], A. Deever and S. Hemami examines sign coding in detail in the contextof an embedded wavelet image coder. The paper shows that a Peak Signal toNoise Ratio (PSNR) improvement up to 0.7 dB is possible when sign entropy

� Thanks to Spanish Ministry of education and Science under grant DPI2007-66796-C03-03 for funding.

J. Cabestany, I. Rojas, and G. Joya (Eds.): IWANN 2011, Part I, LNCS 6691, pp. 505–512, 2011.c© Springer-Verlag Berlin Heidelberg 2011

506 R. Garćıa et al.

coding and a new extrapolation technique based on the mutual information thatbiorthogonal basis vectors provide to improve the estimation of insignificantcoefficients are combined. However, the contribution of sign coding by itself tothe PSNR improvement is only up to 0.4 dB.

In [4] the Embedded Block Coding with Optimized Truncation of the embed-ded bit-streams (EBCOT), core coding tool of the JPEG 2000 standard, encodesthe sign of wavelet coefficients using context information from the sign of hori-zontal and vertical neighbor coefficients (North, South, East, West directions).Five context are used to model the sign coding stage.

In [3], X. Wu presents a high order context modeling encoder. In this coder,the sign and the textures share the same context modeling. This model is basedon a different neighborhood for the HL, LH and HH wavelet subbands. For theHL subband, the information of North, North-West, North-East, North-Northand South sign is used to predict the current coefficient sign. The neighborssign information used for the LH subband are North, North-West, North-East,West-West and East. Finally, for the HH subband, an inter-band prediction isused besides the intra-band prediction used by the HL and LH subbands.

Genetic algorithms were first introduced by Holland in [6] and they are nowa-days well known techniques for finding nearly optimal solutions of very largeproblems and also, they have been used in image processing [7][8].

In a genetic algorithm, the evolution usually starts from a population of ran-domly generated individuals and happens in generations. In each generation,the fitness of every individual in the population is evaluated by means of a costfunction that determines the optimal degree we are looking for (i.e compressionrate). Multiple individuals are stochastically selected from the current popula-tion (based on their fitness), and modified (recombined and possibly randomlymutated) to form a new population. The new population is then used in the nextiteration of the algorithm. Commonly, the algorithm terminates when either amaximum number of generations has been produced, or a satisfactory fitnesslevel has been reached for the population.

In this paper, we will explore the convenience of employing genetic algorithmsto efficiently predict the wavelet coefficient signs. If we find a genetic algorithmthat help us to define a good wavelet sign prediction, then, instead of codingthe sign, we will encode the result of the prediction (i.e success or failure). Ifthe sign prediction is really good, a binary entropy encoder will be able to getsignificant compression rates. So, our goal is to define a genetic algorithm thatfinds out the paremeters of our sign predictor that achieve the best predictionperformance. As studied in the literature, the parameters to be found by ourgenetic algorithm will be a) the neighbor set that defines the prediction context,and b) the sign values (sign patterns) of wavelet coefficient neighbor set withthe correspondent sign prediction for current wavelet coefficient.

After running the genetic algorithm and configured the sign predictor, we willevaluate the impact of the sign coding module in the overall performance of animage wavelet encoder. In particular, we will use the LTW wavelet encoder [9]to determine the bit-rate savings for several test images.

Genetic Algorithm for Wavelet Sign Coding 507

The remainder of the paper is organized as follows: Section 2 describes our signcoding approximation. In Section 3, we show the results of the global encodersystem (with sign coding stage) and compare it with SPIHT and JPEG 2000.Finally, in Section 4 some conclusions are drawn.

2 Wavelet Sign Prediction

Most wavelet image codecs do not consider the use of sign coding tools since thewavelet coefficients located at the high frequency subbands form a zero-meanprocess, and therefore equally likely positive as negative.

Schwartz, Zandi and Boliek were the first authors to consider sign coding,using one neighboring pixel in their context modeling algorithm [10]. The mainidea behind this approach is to find correlations along and across edges.

The HL subbands of a multi-scale 2-D wavelet decomposition are formedfrom low-pass vertical filtering and high-pass horizontal filtering. The high-passfiltering detects vertical edges, thus the HL subbands contain mainly verticaledge information. Oppositely defined are the LH subbands that contain primarilyhorizontal edge information.

As Deever explained in [5], given a vertical edge in an HL subband, it isreasonable to expect that neighboring coefficients along the edge have the samesign as the coefficient being coded. This is because vertical correlation oftenremains very high along vertical edges in images. When a low-pass filter is appliedalong the image columns, it results in a series of similar rows, as elements in arow tend to be very similar to elements directly above or below due to the highvertical correlation. Subsequent high-pass filtering along similar rows is expectedto yield vertically correlated transform coefficients.

It is also important to consider correlation across edges, being the natureof the correlation directly affected by the structure of the high pass filter. ForDaubechies’ 9/7 filters, wavelet coefficient signs are strongly negatively corre-lated across edges because this filter is very similar to a second derivative of aGaussian, so, it is expected that wavelet coefficients will change sign as the edgeis crossed. Although the discrete wavelet transform involves sub sampling, thesub sampled coefficients remain strongly negatively correlated across edges. Inthis manner, when a wavelet coefficient is optimally predicted as a function of itsacross-edge neighbors (e.g. left and right neighbors in HL subbands), the opti-mal prediction coefficients are negative, indicating an expected sign change. Thisconclusion is general for any wavelet with a shape similar to a second derivativeof a Gaussian.

To estimate sign correlation in a practical way, we have applied a 6-levelDyadic Wavelet Transform decomposition of the source image and then a lowquantization level to the resulting wavelet coefficients. As a first approach andtaking into account that the sign neighborhood correlation depends on the sub-band type (HL,LH,HH) as Deever assesses in [5], we have used three differentneighbors depending on the subband type. So, for HL subband, the neighborsused are N, NN and W. Taking into account symmetry, for the LH subband,


those neighbors are W, WW, and N. For the HH subband they are N, W, andNW, exploiting the correlation along and across the diagonal edges. This leadus to a maximum of 33 Neighbor Sign Patterns (NSP) for each subband type.

Table 1. Probability distribution of neighbor sign patterns (NSPs) of HL6 subband(8x8 coefficients) in Lena image

C N NN W Occurrences %Probability

+ + + + 13 20.31+ + + - 8 12.50- - - + 8 12.50- + + + 6 9.38- - + + 6 9.38

Others 23 35.93

In Table 1 we show the NSP probability distribution for HL6 subband (fromthe sixth decomposition level) of Lena test image. As shown, the probabilitythat the current coefficient (C) is positive when its N, NN and W neighborsare also positive is around 20%. Besides, if the N and NN neighbors have thesame sign and the W neighbor has the opposite sign, the current coefficient (C)has the opposite sign of its W neighbor with a probability of 25% as shown inrows two and three in Table 1. The visible sign neighborhood correlation suggestthat the sign bits of wavelet coefficients are compressible. Using the previouslymentioned neighborhood for each subband type, we have developed a geneticalgorithm (GA) in order to find an accurate sign estimation.

2.1 Genetic Algorithm for Wavelet Sign Prediction

The goal of the desired genetic algorithm would be to find a table where for eachSign Neigborhood Pattern (Vk) we have a sign prediction (Si,j) for coefficient Ci,j. There is no an univocal relationship between a neighbor sign combination, i.enot always for a same Vk pattern, Si,j is always positive or negative. However,it is possible that for a Vk pattern, Si,j is more probably to be positive ornegative. But, the problem is still more complex, because a sign prediction for aneighbor sign pattern could fit well for an image and not for others. Therefore,the idea is to find suboptimal neighbor sign pattern predictions that better fitfor a representative set of images.

The use of genetic algorithms to compress the sign of wavelet coefficients istwofold. First, when the number of neighbors used to analyze the sign correlationgrows or when there is a great number of images to be used in the analysis, thesearch space is excessively wide. Second, it is not intuitive to find a way ofcombining the predictions obtained for several images.

In Fig. 1 we show the genetic algorithm pseudocode for sign prediction. Firstof all we define each individual, containing a sign prediction for each 33 NSP, then


each NSP sign prediction of each individual of the universe is randomly initial-ized as a positive or negative sign. Then, during evolution, sequences mate andmutate to generate new sequences in the population and best sequences are se-lected for survival on the basis of their fitness function. The mating of sequencesis performed through crossover operator, where parents are randomly selectedand its gens (NSPs) are mixed. The best two individuals, the ones that exhibitbest prediction performance, are selected for survival. Individuals can also un-dergo mutation, where a sequence prediction is randomly modified. Finally, afterperforming the maximum iterations, the algorithm finishes, obtaining an opti-mal/suboptimal sign prediction for each NSP. We have performed the fitnessevaluation over Lena and Barbara test images, because these images are repre-sentative for both low and high textured images respectively. Several parametersshould be taken into account when training a genetic algorithm: The populationsize, the individuals initialization, the number of iterations performed, the muta-tion probability, the crossover point, the crossover method, the selection criteriaof the best sequences to be selected for survival, etc. We have performed lots oftests varying these parameters to tune the genetic algorithm. The parametersused to obtain the sign prediction are: population size (100), individuals initial-ization (ramdomly), number of iterations (1000), mutation probability (0.001),crossover point (ramdomly) and crossover method (best two fitness individualsover four randomly selected parents).

Individual Structure{sign[NSP];//Prediction array for each neighbor sign pattern combinationfitness; //indicates the goodness of the individual}Individual universe[NUM-POPULATION]; //Individual array

function SignPrediction (SubbandType, ImageFiles, mutation Probability)//Initialization phase: sign[NSPs]= random(POSITIVE/NEGATIVE)Initialize(universe, NUM-POPULATION, NSP);//we evaluate each individual of the universe. For each image in ImageFilesEvaluateFitness(SubbandType, ImageFiles, universe);for i=0 to NUM-ITERATIONS//Select the best two individuals from universe for survival.

best = SelectBestIndividuals(2);//CrossovercrossPoint=random(NSP);//randomly selects a father and a mother to mix its gensSelectFatherAndMother(random(NUM-POLUTATION));universe = MergeFatherAndMother(crossPoint);Mutation(universe, mutation Probability);universe = universe + best;EvaluateFitness(SubbandType, ImageFiles, universe);

end//Finally get the best individual.best = SelectBestIndividuals(1);

end of function

Fig. 1. Genetic algorithm for sign prediction


After running the genetic algorithm for each subband type, we obtain anindividual containing the prediction of the current coefficient sign (ŜCi,j [k]),for each NSP (k) of each subband type. So, what we are going to encode isthe correctness of this prediction, i.e., a binary valued symbol from ŜCi,j [k] ·SCi,j (see Table 2). In order to compress this binary valued symbol, we use twocontexts in the arithmetic encoder for each subband type, distributing all signcoding predictions from NSPs between them so as to minimize the zero orderentropy of both contexts. The selection criterion is to isolate in one context thoseNSPs with the highest correctness prediction probability and highest numberof occurrences derived from the probability distribution found in the previousanalysis. The rest of them are grouped into the other context. However, thereare certain NSPs with low correctness probability but with a great amount ofoccurrences, so we have to heuristically determine the convenience of includingthem in the first context or not.

Table 2. Sign prediction for HL subband in Lena image for some NSPs

NSP(k) N NN W Prediction

(ŜCi,j [k])

0 * * * -. . .

13 + + + +14 + + - +

. . .26 - - - +

3 Performance Evaluation

In this section we analyze the behavior of the sign coding when implemented onLTW image encoder [9]. This new encoder implementation is called S-LTW. Wewill also compare the S-LTW encoder versus JPEG2000 (Jasper 1.701.0) andSPIHT (Spiht 8.01) in terms of R/D and coding delay. All encoders have beentested on an Intel PentiumM Dual Core 3.0 GHz with 2 Gbyte RAM memory.

In Table 3 we show the relative compression gain with respect to the originalLTW due only to the sign coding capability for several test images. As we cansee, the maximum sign compression gain is 17.35%. Furthermore, we show anestimation of the bit savings for SPIHT encoder.

In Figure 2 we show the R/D improvement when comparing original LTW ver-sus JPEG2000/SPIHT and S-LTW versus JPEG2000/SPIHT. As shown, thereis an increase in the PSNR difference between SPIHT and the new S-LTW en-coder, and regarding JPEG2000, we can see than now S-LTW has a minor lossin PSNR than original LTW. Regarding coding delay, the use of a higher contextmodeling in the arithmetic encoder implies a higher computational cost. In orderto compensate the coding speed loss, we have changed the arithmetic encoderstage by a fast arithmetic encoder [11]. As it can be seen in Table 4, S-LTW


0.4

0.5 S-LTW vs SPIHTLTW vs SPIHT

0.3S-LTW vs JPEG2000LTW vs JPEG2000

0.1

0.2

dB)

0 1

00 0.5 1 1.5 2PS

NR

(d

-0.2

-0.1P

-0.4

-0.3

-0.5

0.4

Bit-rate (bpp)

Fig. 2. PSNR-Gain for Bike image

Table 3. Sign compression performance at different bit-rates

Bit-rate S-LTW SPIHT %Gain(bpp) #Significant #Bits #Significant #Bits

Coefficients Saved Coefficients Saved

Barbara (512x512)

1 45740 7936 54657 9482 17.350.5 22331 3648 27535 4499 16.340.25 10484 1520 13460 1951 14.500.125 4343 304 6016 421 7.00

Bike (2048x2560)

1 855266 115200 1371280 184711 13.470.5 412212 64424 798202 124758 15.630.25 198943 30472 366927 56213 15.320.125 91767 11992 162990 21302 13.07

Table 4. Coding delay (seconds)

Bit-rate JPEG SPIHT LTW S-LTW(bpp) 2000 Orig.

CODING Barbara (512x512)1 0.080 0.042 0.037 0.023

0.5 0.076 0.026 0.022 0.0140.25 0.074 0.018 0.013 0.0090.125 0.073 0.014 0.010 0.006

CODING Bike (2048x2560)1 2.623 0.920 0.647 0.430

0.5 2.543 0.521 0.381 0.2590.25 2.507 0.323 0.224 0.1620.125 2.518 0.221 0.158 0.117


encoder is 49% faster on average in the coding process than SPIHT encoder and86% faster on average than JPEG2000. Furthermore, S-LTW encoder is evenfaster than the original LTW version which does not include the sign codingstage (1.5 times faster on average in the coding process).

4 Conclusions

We have presented a genetic algorithm that is able to find a good sign predictorof wavelet coefficient sign. So, by encoding the sign prediction result (successor failure) with an arithmetic encoder, the sign information will be highly com-pacted in the final bitstream. To prove our proposal we have implemented itover the LTW encoder. The new S-LTW proposed encoder has slightly betterR/D performance(up to 0.25 dB), or in terms of bitstream, it is able to reduceit up to 17% for the same quality level. Regarding coding delay, the new imageencoder is on average 2 times as fast as SPIHT in the coding process and 1.5times as fast as original LTW.

References

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front-matter66910505On the Use of Genetic Algorithms to Improve Wavelet Sign Coding PerformanceIntroductionWavelet Sign PredictionGenetic Algorithm for Wavelet Sign Prediction

Performance EvaluationConclusionsReferences

Documents

Lecture Notes in Computer Science 6691 · 2011. 7. 25. · Lecture Notes in Computer Science 6691 Commenced Publication in 1973 Founding and Former Series Editors: ... izations in