Lecture Notes in Computer Science 3776Commenced 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

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 SteffenUniversity of Dortmund, Germany

Madhu SudanMassachusetts Institute of Technology, MA, USA

Demetri TerzopoulosNew York University, NY, USA

Doug TygarUniversity of California, Berkeley, CA, USA

Moshe Y. VardiRice University, Houston, TX, USA

Gerhard WeikumMax-Planck Institute of Computer Science, Saarbruecken, Germany

Sankar K. Pal Sanghamitra BandyopadhyaySambhunath Biswas (Eds.)

Pattern Recognitionand Machine Intelligence

First International Conference, PReMI 2005Kolkata, India, December 20-22, 2005Proceedings

1 3

Volume Editors

Sankar K. PalIndian Statistical InstituteKolkata 700 108, IndiaE-mail: sankar@isical.ac.in

Sanghamitra BandyopadhyaySambhunath BiswasIndian Statistical Institute, Machine Intelligence UnitKolkata 700 108, IndiaE-mail: {sanghami,sambhu}@isical.ac.in

Library of Congress Control Number: 2005937700

CR Subject Classification (1998): I.4, F.1, I.2, I.5, J.3, C.2.1, C.1.3

ISSN 0302-9743ISBN-10 3-540-30506-8 Springer Berlin Heidelberg New YorkISBN-13 978-3-540-30506-4 Springer Berlin Heidelberg New York

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Message from the General Chair

Pattern recognition and machine intelligence form a major area of research anddevelopmental activity that encompasses the processing of multimedia informa-tion obtained from the interaction between science, technology and society. Animportant motivation for the spurt of activity in this field is the desire to de-sign and make intelligent machines capable of performing certain tasks that wehuman beings do. Potential applications exist in forefront research areas likecomputational biology, data mining, Web mining, global positioning systems,medical imaging, forensic sciences, besides classical problems of optical charac-ter recognition, biometry, target recognition, face recognition, remotely senseddata analysis, and man–machine communication. There have been several con-ferences around the world in the past few decades individually in these two areasof pattern recognition and artificial intelligence, but hardly any combining thetwo, although both communities share similar objectives. Therefore holding thisinternational conference covering these domains is very appropriate and timely,considering the recent needs of information technology, which is a key vehicle forthe economic development of any country. The first integrated meeting of 2005,under this theme, was PReMI 2005.

The objective of this international meeting is to present state-of-the-art sci-entific results, encourage academic and industrial interaction, and to promotecollaborative research and developmental activities, in pattern recognition, ma-chine intelligence and related fields, involving scientists, engineers, professionals,researchers and students from India and abroad. The conference will be heldevery two years to make it an ideal platform for people to share their views andexperiences in these areas. Particular emphasis in PReMI 2005 was placed oncomputational biology, data mining and knowledge discovery, soft computing,case-based reasoning, biometry, as well as various upcoming pattern recogni-tion/image processing problems. There were tutorials, keynote talks and invitedtalks, delivered by speakers of international repute from both academia and in-dustry. These were accompanied by interesting special sessions apart from theregular technical sessions.

It may be mentioned here that in India the activity on pattern recogni-tion started in the 1960s, mainly in ISI, Calcutta, TIFR, Bombay and IISc,Bangalore. ISI, having a long tradition of conducting basic research in statis-tics/mathematics and related areas, has been able to develop profoundly ac-tivities in pattern recognition and machine learning in its different facets anddimensions with real-life applications. These activities have subsequently beenconsolidated under a few separate units in one division. As a mark of the signif-icant achievements, special mention may be made of the DOE-sponsored KBCSNodal Center of ISI founded in the 1980s and the Center for Soft ComputingResearch (CSCR) of ISI established in 2004 by the DST, Government of India.

VI Preface

CSCR, which is the first national center in the country in this domain, has manyimportant objectives including distance learning, establishing linkage to premierinstitutes/industries, organizing specialized courses, as well as conducting fun-damental research.

The conference proceedings of PReMI-05, containing rigorously reviewed pa-pers, is published by Springer in its prestigious Lecture Notes in Computer Sci-ence (LNCS) series. Different professional sponsors and funding agencies (bothnational and international) came forward to support this event for its success.These include, International Association of Pattern Recognition (IAPR); WebIntelligence Consortium (WIC); Institute of Electrical and Electronics Engineer-ing (IEEE): International Center for Pure and Applied Mathematics (CIMPA),France; Webel, Government of West Bengal IT Company; Department of Science& Technology (DST), India; Council of Scientific & Industrial Research (CSIR),India. To encourage participation of bright students and young researchers, somefellowships were provided.

I believe the participants found PReMI-05 an academically memorable andintellectually stimulating event. It enabled young researchers to interact andestablish contacts with well-known experts in the field

I hope that you have all enjoyed staying in Calcutta (now Kolkata), the cityof Joy.

September 2005 Sankar K. Pal, KolkataGeneral Chair, PReMI-05

Director, Indian Statistical Institute

Preface

This volume contains the papers selected for the 1st International Conference onPattern Recognition and Machine Intelligence (PReMI 2005), held in the IndianStatistical Institute Kolkata, India during December 18–22, 2005. The primarygoal of the conference was to present the state-of-the-art scientific results, en-courage academic and industrial interaction, and promote collaborative researchand developmental activities in pattern recognition, machine intelligence andrelated fields, involving scientists, engineers, professionals, researchers and stu-dents from India and abroad. The conference will be held every two years tomake it an ideal platform for people to share their views and experiences inthese areas.

The conference had five keynote lectures, 16 invited talks and an eveningtalk, all by very eminent and distinguished researchers from around the world.The conference received around 250 submissions from 24 countries spanningsix continents. Each paper was critically reviewed by two experts in the field,after which about 90 papers were accepted for oral and poster presentations.In addition, there were four special sessions organized by eminent researchers.Accepted papers were divided into 11 groups, although there could be someoverlap.

We take this opportunity to express our gratitude to Professors A. K. Jain,R. Chellappa, D. W. Aha, A. Skowron and M. Zhang for accepting our invi-tation to be the keynote speakers in this conference. We thank Professors B.Bhattacharya, L. Bruzzone, A. Buller, S. K. Das, U. B. Desai, V. D. Gesu, M.Gokmen, J. Hendler, J. Liu, B. Lovell, J. K. Udupa, M. Zaki, D. Zhang andN. Zhong for accepting our invitation to present invited papers in this confer-ence. Our special thanks are due to Professor J. K. Aggarwal for the acceptanceof our invitation to deliver a special evening lecture. We also express our deepappreciation to Professors Dominik Slezak, Carlos Augusto Paiva da Silva Mar-tins, P. Nagabhushan and Kalyanmoy Gupta for organizing interesting specialsessions during the conference. We gratefully acknowledge Mr. Alfred Hofmann,Executive Editor, Computer Science Editorial, Springer, Heidelberg, Germany,for extending his co-operation for publication of the PReMI-05 proceedings. Fi-nally, we would like to thank all the contributors for their enthusiastic response.

We hope that the conference was an enjoyable and academically fruitful meet-ing for all the participants.

September 2005 Sankar K. PalSanghamitra Bandypodhyay

Sambhunath BiswasEditors

Organization

PReMI 2005 was organized by the Machine Intelligence Unit, Indian StatisticalInstitute (ISI) in Kolkata during December 18–22, 2005.

PReMI 2005 Conference Committee

General Chair: Sankar K. Pal (ISI, Kolkata, India)Program Chairs: S. Mitra (ISI, Kolkata, India)

S. Bandyopadhyay (ISI, Kolkata, India)W. Pedrycz (University of Alberta, Edmonton,Canada)

Organizing Chairs: C. A. Murthy (ISI, Kolkata, India)R. K. De (ISI, Kolkata, India)

Tutorial Chair: A. Ghosh (ISI, Kolkata, India)Publication Chair: S. Biswas (ISI, Kolkata, India)Industrial Liaison: M. K. Kundu (ISI, Kolkata, India)

R. Roy (PWC, India)International Liaison: G. Chakraborty (Iwate Prefectural University,

Takizawamura, Japan)J. Ghosh (University of Texas at Austin, USA)

Advisory Committee

A. K. Jain, USAA. Kandel, USAA. P. Mitra, IndiaA. Skowron, PolandB. L. Deekshatulu, IndiaC. Y. Suen, CanadaD. Dutta Majumder, IndiaH. Bunke, SwitzerlandH.-J. Zimmermann, GermanyJ. Keller, USAJ. K. Ghosh, IndiaJ. M. Zurada, USAK. Kasturirangan, IndiaL. Kanal, USAL. A. Zadeh, USAM. G. K. Menon, IndiaM. Jambu, FranceM. Vidyasagar, India

X Organization

N. Balakrishnan, IndiaR. Chellappa, USAR. A. Mashelkar, IndiaS. Prasad, IndiaS. I. Amari, JapanT. S. Dillon, AustraliaT. Yamakawa, JapanV. S. Ramamurthy, IndiaZ. Pawlak, Poland

Program Committee

A. Pal, IndiaA. K. Majumdar, IndiaA. M. Alimi, TunisiaB. B. Bhattacharyya, IndiaB. Chanda, IndiaB. Lovell, AustraliaB. Yegnanarayana, IndiaD. Mukhopadhyay, IndiaD. Slezak, CanadaD. Zhang, Hong Kong, ChinaD. P. Mukherjee, IndiaD. W. Aha, USAE. Diday, FranceG. S. di Baja, ItalyH. Frigui, USAH. Kargupta, USAH. L. Larsen, DenmarkJ. Basak, IndiaJ. K. Udupa, USAL. Bruzzone, ItalyL. Hall, USAL. I. Kuncheva, UKM. Banerjee, IndiaM. Nikravesh, USAM. N. Murty, IndiaN. Nasrabadi, USAN. Zhong, JapanO. Nasraoui, USAO. Vikas, IndiaP. Chaudhuri, IndiaP. Mitra, IndiaR. Kothari, IndiaR. Setiono, Singapore

Organization XI

R. Krishnapuram, IndiaR. Weber, ChileS. Bose, IndiaS. B. Cho, KoreaSantanu Chaudhuri, IndiaSubhasis Chaudhuri, IndiaS. Mukhopadhyay, IndiaS. Sarawagi, IndiaS. Ray, AustraliaS. Sinha, IndiaS. K. Parui, IndiaT. Heskes, NetherlandsT. K. Ho, USAU. B. Desai, IndiaV. D. Gesu, ItalyY. Hayashi, JapanY. V. Venkatesh, SingaporeY. Y. Tang, Hong Kong, China

Reviewers

A. M. AlimiA. BagchiA. BhaduriA. BishnuA. BiswasA. GhoshA. KonarA. LahaA. LahiriA. LiuA. K. MajumdarA. MukhopadhyayA. NegiA. PalB. B. BhattacharyyaB. ChandaB. LovellB. RajuC. A. MurthyC. V. JawaharD. W. AhaD. ChakrabortyD. M. Akbar Hussain

D. MukhopadhyayD. P. MukherjeeD. SlezakD. ZhangE. DidayF. R. B. CruzF. A. C. GomideG. S. di BajaG. ChakrabortyG. GuptaG. PedersenG. SiegmonH. FriguiH. KarguptaH. L. LarsenJ. BasakJ. GhoshJ. MukhopadhyayJ. K. SingJ. SilJ. K. UdupaJayadevaK. Kummamuru

K. MaliK. PolthierK. S. VenkateshL. BruzzoneL. DeyL. HallL. I. KunchevaL. V. SubramaniamL. E. ZarateM. AcharyyaMahua BanerjeeMinakshi BanerjeeM. DeodharM. GehrkeM. K. KunduM. MandalM. MitraM. N. MurtyM. NasipuriM. K. PakhiraM. P. SriramN. DasN. Zhong

XII Organization

O. NasraouiP. BhowmickP. BiswasP. ChaudhuriP. DasguptaP. DuttaP. GuhaP. MitraP. P. MohantaP. NagabhushanP. A. VijayaRajasekharR. KrishnapunamR. SetionoR. KothariR. WeberR. K. DeR. U. UdupaR. M. LotlikarR. H. C. TakashiSameenaS. Asharaf

S. BandyopadhyayS. BiswasS. BoseSantanu ChaudhuriSubhasis ChaudhuriS. DattaS. GhoshS. MaitraS. MitraS. MukhopadhyayS. MukherjeeS. PalitS. RayS. RoychowdhuryS. SarawagiS. SandhyaS. SenS. SinhaS. Sur-KolayS. B. ChoS. K. DasS. K. Mitra

S. K. PalS. K. ParuiT. AcharyaT. A. FaruquieT. HeskesT. K. HoT. PalU. BhattacharyyaU. B. DesaiU. GarainU. PalV. S. DeviV. D. GesuV. A. PopovV. V. SaradhiW. PedryczY. Y. TangY. HayashiY. V. VenkateshY. Zhang

Sponsoring Organizations

– Indian Statistical Institute (ISI), Kolkata– Center for Soft Computing Research-A National Facility, ISI, Kolkata– Department of Science and Technology, Government of India– International Center for Pure and Applied Mathematics (CIMPA), France– International Association for Pattern Recognition (IAPR)– Web Intelligence Consortium (WIC), Japan– Webel, Government of West Bengal IT Company, India– Council of Scientific & Industrial Research (CSIR), Government of India– Institute of Electrical and Electronics Engineers (IEEE), USA– And others

Table of Contents

Keynote Papers

Data Clustering: A User’s DilemmaAnil K. Jain, Martin H.C. Law . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Pattern Recognition in VideoRama Chellappa, Ashok Veeraraghavan, Gaurav Aggarwal . . . . . . . . . . 11

Rough Sets in Perception-Based ComputingAndrzej Skowron . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

Conversational Case-Based ReasoningDavid W. Aha . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

Computational Molecular Biology of Genome Expression andRegulation

Michael Q. Zhang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

Human Activity RecognitionJ.K. Aggarwal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

Invited Papers

A Novel T2-SVM for Partially Supervised ClassificationLorenzo Bruzzone, Mattia Marconcini . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

S Kernel: A New Symmetry MeasureVito Di Gesu, Bertrand Zavidovique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

Geometric Decision Rules for Instance-Based Learning ProblemsBinay Bhattacharya, Kaustav Mukherjee,Godfried Toussaint . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

Building Brains for Robots: A Psychodynamic ApproachAndrzej Buller . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

Designing Smart Environments: A Paradigm Based on Learning andPrediction

Sajal K. Das, Diane J. Cook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80

XIV Table of Contents

Towards Generic Pattern MiningMohammed J. Zaki, Nilanjana De, Feng Gao, Nagender Parimi,Benjarath Phoophakdee, Joe Urban Vineet Chaoji,Mohammad Al Hasan, Saeed Salem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91

Multi-aspect Data Analysis in Brain InformaticsNing Zhong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98

Small Object Detection and Tracking: Algorithm, Analysis andApplication

U.B. Desai, S.N. Merchant, Mukesh Zaveri, G. Ajishna,Manoj Purohit, H.S. Phanish . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108

Illumination Invariant Face Alignment Using Multi-band ActiveAppearance Model

Fatih Kahraman, Muhittin Gokmen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118

Globally Optimal 3D Image Reconstruction and Segmentation ViaEnergy Minimisation Techniques

Brian C. Lovell . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128

Go Digital, Go FuzzyJayaram K. Udupa, George J. Grevera . . . . . . . . . . . . . . . . . . . . . . . . . . . 137

A Novel Personal Authentication System Using Palmprint TechnologyDavid Zhang, Guangming Lu, Adams Wai-Kin Kong,Michael Wong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147

World Wide Wisdom Web (W4) and Autonomy Oriented Computing(AOC): What, When, and How?

Jiming Liu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157

Semantic Web Research Trends and DirectionsJennifer Golbeck, Bernardo Cuenca Grau,Christian Halaschek-Wiener, Aditya Kalyanpur, Bijan Parsia,Andrew Schain, Evren Sirin, James Hendler . . . . . . . . . . . . . . . . . . . . . 160

Contributory Papers

Clustering, Feature Selection and Learning

Feature Extraction for Nonlinear ClassificationAnil Kumar Ghosh, Smarajit Bose . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170

Table of Contents XV

Anomaly Detection in a Multi-engine AircraftDinkar Mylaraswamy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 176

Clustering Within Quantum Mechanical FrameworkGuleser K. Demir . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182

Linear Penalization Support Vector Machines for Feature SelectionJaime Miranda, Ricardo Montoya, Richard Weber . . . . . . . . . . . . . . . . 188

Linear Regression for Dimensionality Reduction and Classification ofMulti Dimensional Data

Lalitha Rangarajan, P. Nagabhushan . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193

A New Approach for High-Dimensional Unsupervised Learning:Applications to Image Restoration

Nizar Bouguila, Djemel Ziou . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 200

Unsupervised Classification of Remote Sensing Data Using GraphCut-Based Initialization

Mayank Tyagi, Ankit K Mehra, Subhasis Chaudhuri,Lorenzo Bruzzone . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 206

Hybrid Hierarchical Learning from Dynamic ScenesPrithwijit Guha, Pradeep Vaghela, Pabitra Mitra, K.S. Venkatesh,Amitabha Mukerjee . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 212

Reference Extraction and Resolution for Legal TextsMercedes Martınez-Gonzalez, Pablo de la Fuente,Damaso-Javier Vicente . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 218

Classification

Effective Intrusion Type Identification with Edit Distance forHMM-Based Anomaly Detection System

Ja-Min Koo, Sung-Bae Cho . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 222

A Combined fBm and PPCA Based Signal Model for On-LineRecognition of PD Signal

Pradeep Kumar Shetty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 229

Handwritten Bangla Digit Recognition Using Classifier CombinationThrough DS Technique

Subhadip Basu, Ram Sarkar, Nibaran Das, Mahantapas Kundu,Mita Nasipuri, Dipak Kumar Basu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 236

XVI Table of Contents

Arrhythmia Classification Using Local Holder Exponents and SupportVector Machine

Aniruddha Joshi, Rajshekhar, Sharat Chandran, Sanjay Phadke,V.K. Jayaraman, B.D. Kulkarni . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 242

A Voltage Sag Pattern Classification TechniqueDelio E.B. Fernandes, Mario Fabiano Alvaes,Pyramo Pires da Costa Jr. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 248

Face Recognition Using Topological Manifolds LearningCao Wenming, Lu Fei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 254

A Context-Sensitive Technique Based on Support Vector Machines forImage Classification

Francesca Bovolo, Lorenzo Bruzzone . . . . . . . . . . . . . . . . . . . . . . . . . . . . 260

Face Recognition Technique Using Symbolic PCA MethodP.S. Hiremath, C.J. Prabhakar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 266

A Hybrid Approach to Speaker Recognition in Multi-speakerEnvironment

Jigish Trivedi, Anutosh Maitra, Suman K. Mitra . . . . . . . . . . . . . . . . . . 272

Design of Hierarchical Classifier with Hybrid ArchitecturesM.N.S.S.K. Pavan Kumar, C.V. Jawahar . . . . . . . . . . . . . . . . . . . . . . . . 276

Neural Networks and Applications

Human-Computer Interaction System with Artificial Neural NetworkUsing Motion Tracker and Data Glove

Cemil Oz, Ming C. Leu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 280

Recurrent Neural Approaches for Power Transformers Thermal ModelingMichel Hell, Luiz Secco, Pyramo Costa Jr.,Fernando Gomide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 287

Artificial Neural Network Engine: Parallel and ParameterizedArchitecture Implemented in FPGA

Milene Barbosa Carvalho, Alexandre Marques Amaral,Luiz Eduardo da Silva Ramos,Carlos Augusto Paiva da Silva Martins, Petr Ekel . . . . . . . . . . . . . . . . 294

Neuronal Clustering of Brain fMRI ImagesNicolas Lachiche, Jean Hommet, Jerzy Korczak,Agnes Braud . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 300

Table of Contents XVII

An Optimum RBF Network for Signal Detection in Non-GaussianNoise

D.G. Khairnar, S.N. Merchant, U.B. Desai . . . . . . . . . . . . . . . . . . . . . . 306

A Holistic Classification System for Check Amounts Based on NeuralNetworks with Rejection

M.J. Castro, W. Dıaz, F.J. Ferri, J. Ruiz-Pinales, R. Jaime-Rivas,F. Blat, S. Espana, P. Aibar, S. Grau, D. Griol . . . . . . . . . . . . . . . . . . 310

A Novel 3D Face Recognition Algorithm Using Template BasedRegistration Strategy and Artificial Neural Networks

Ben Niu, Simon Chi Keung Shiu, Sankar Kumar Pal . . . . . . . . . . . . . . 315

Fuzzy Logic and Applications

Recognition of Fault Signature Patterns Using Fuzzy Logic forPrevention of Breakdowns in Steel Continuous Casting Process

Arya K. Bhattacharya, P.S. Srinivas, K. Chithra, S.V. Jatla,Jadav Das . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 318

Development of an Adaptive Fuzzy Logic Based Control Law for aMobile Robot with an Uncalibrated Camera System

T. Das, I.N. Kar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 325

Fuzzy Logic Based Control of Voltage and Reactive Power inSubtransmission System

Ana Paula M. Braga, Ricardo Carnevalli,Petr Ekel, Marcelo Gontijo, Marcio Junges,Bernadete Maria de Mendonca Neta, Reinaldo M. Palhares . . . . . . . . 332

Fuzzy-Symbolic Analysis for Classification of Symbolic DataM.S. Dinesh, K.C. Gowda, P. Nagabhushan . . . . . . . . . . . . . . . . . . . . . . 338

Handwritten Character Recognition Systems Using Image-Fusion andFuzzy Logic

Rupsa Chakraborty, Jaya Sil . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 344

Classification of Remotely Sensed Images Using Neural-NetworkEnsemble and Fuzzy Integration

G. Mallikarjun Reddy, B. Krishna Mohan . . . . . . . . . . . . . . . . . . . . . . . . 350

Intelligent Learning Rules for Fuzzy Control of a Vibrating ScreenClaudio Ponce, Ernesto Ponce . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 356

XVIII Table of Contents

Fuzzy Proximal Support Vector Classification Via GeneralizedEigenvalues

Jayadeva, Reshma Khemchandani, Suresh Chandra . . . . . . . . . . . . . . . 360

Segmentation of MR Images of the Human Brain Using Fuzzy AdaptiveRadial Basis Function Neural Network

J.K. Sing, D.K. Basu, M. Nasipuri, M. Kundu . . . . . . . . . . . . . . . . . . . 364

Optimization and Representation

Design of Two-Dimensional IIR Filters Using an Improved DEAlgorithm

Swagatam Das, Debangshu Dey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 369

Systematically Evolving Configuration Parameters for ComputationalIntelligence Methods

Jason M. Proctor, Rosina Weber . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 376

A Split-Based Method for Polygonal Approximation of Shape CurvesR. Dinesh, Santhosh S. Damle, D.S. Guru . . . . . . . . . . . . . . . . . . . . . . . 382

Symbolic Data Structure for Postal Address Representation andAddress Validation Through Symbolic Knowledge Base

P. Nagabhushan, S.A. Angadi, B.S. Anami . . . . . . . . . . . . . . . . . . . . . . . 388

The Linear Factorial Smoothing for the Analysis of Incomplete DataBasavanneppa Tallur . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 395

An Improved Shape Descriptor Using Bezier CurvesFerdous Ahmed Sohel, Gour C. Karmakar,Laurence S. Dooley . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 401

Isothetic Polygonal Approximations of a 2D Object on GeneralizedGrid

Partha Bhowmick, Arindam Biswas, Bhargab B. Bhattacharya . . . . . . 407

An Evolutionary SPDE Breeding-Based Hybrid Particle SwarmOptimizer: Application in Coordination of Robot Ants for CameraCoverage Area Optimization

Debraj De, Sonai Ray, Amit Konar, Amita Chatterjee . . . . . . . . . . . . . 413

An Improved Differential Evolution Scheme for Noisy OptimizationProblems

Swagatam Das, Amit Konar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 417

Table of Contents XIX

Image Processing and Analysis

Facial Asymmetry in Frequency Domain: The “Phase” ConnectionSinjini Mitra, Marios Savvides, B.V.K. Vijaya Kumar . . . . . . . . . . . . . 422

An Efficient Image Distortion Correction Method for an X-ray DigitalTomosynthesis System

J.Y. Kim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 428

Eigen Transformation Based Edge Detector for Gray ImagesP. Nagabhushan, D.S. Guru, B.H. Shekar . . . . . . . . . . . . . . . . . . . . . . . . 434

Signal Processing for Digital Image Enhancement Considering APL inPDP

Soo-Wook Jang, Se-Jin Pyo, Eun-Su Kim, Sung-Hak Lee,Kyu-Ik Sohng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 441

A Hybrid Approach to Digital Image Watermarking Using SingularValue Decomposition and Spread Spectrum

Kunal Bhandari, Suman K. Mitra, Ashish Jadhav . . . . . . . . . . . . . . . . . 447

Image Enhancement by High-Order Gaussian Derivative FiltersSimulating Non-classical Receptive Fields in the Human Visual System

Kuntal Ghosh, Sandip Sarkar, Kamales Bhaumik . . . . . . . . . . . . . . . . . 453

A Chosen Plaintext Steganalysis of Hide4PGP V 2.0Debasis Mazumdar, Soma Mitra, Sonali Dhali, Sankar K. Pal . . . . . . 459

Isolation of Lung Cancer from InflammationMd. Jahangir Alam, Sid Ray, Hiromitsu Hama . . . . . . . . . . . . . . . . . . . 465

Learning to Segment Document ImagesK.S. Sesh Kumar, Anoop Namboodiri, C.V. Jawahar . . . . . . . . . . . . . . 471

A Comparative Study of Different Texture Segmentation TechniquesMadasu Hanmandlu, Shilpa Agarwal, Anirban Das . . . . . . . . . . . . . . . . 477

Run Length Based Steganography for Binary ImagesSos.S. Agaian, Ravindranath C. Cherukuri . . . . . . . . . . . . . . . . . . . . . . . 481

Video Processing and Computer Vision

An Edge-Based Moving Object Detection for Video SurveillanceM. Julius Hossain, Oksam Chae . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 485

XX Table of Contents

An Information Hiding Framework for Lightweight Mobile Devices withDigital Camera

Subhamoy Maitra, Tanmoy Kanti Das, Jianying Zhou . . . . . . . . . . . . . 491

Applications of the Discrete Hodge Helmholtz Decomposition to Imageand Video Processing

Biswaroop Palit, Anup Basu, Mrinal K. Mandal . . . . . . . . . . . . . . . . . . 497

A Novel CAM for the Luminance Levels in the Same ChromaticityViewing Conditions

Soo-Wook Jang, Eun-Su Kim, Sung-Hak Lee, Kyu-Ik Sohng . . . . . . . . 503

Estimation of 2D Motion Trajectories from Video Object Planes andIts Application in Hand Gesture Recognition

M.K. Bhuyan, D. Ghosh, P.K. Bora . . . . . . . . . . . . . . . . . . . . . . . . . . . . 509

3D Facial Pose Tracking in Uncalibrated VideosGaurav Aggarwal, Ashok Veeraraghavan, Rama Chellappa . . . . . . . . . . 515

Fusing Depth and Video Using Rao-Blackwellized Particle FilterAmit Agrawal, Rama Chellappa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 521

Specifying Spatio Temporal Relations for Multimedia OntologiesKarthik Thatipamula, Santanu Chaudhury, Hiranmay Ghosh . . . . . . . 527

Motion Estimation of Elastic Articulated Objects from Image Pointsand Contours

Hailang Pan, Yuncai Liu, Lei Shi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 533

Parsing News Video Using Integrated Audio-Video FeaturesS. Kalyan Krishna, Raghav Subbarao, Santanu Chaudhury,Arun Kumar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 538

Image Retrieval and Data Mining

A Hybrid Data and Space Partitioning Technique for Similarity Querieson Bounded Clusters

Piyush K. Bhunre, C.A. Murthy, Arijit Bishnu,Bhargab B. Bhattacharya, Malay K. Kundu . . . . . . . . . . . . . . . . . . . . . . 544

Image Retrieval by Content Using Segmentation ApproachBhogeswar Borah, Dhruba K. Bhattacharyya . . . . . . . . . . . . . . . . . . . . . 551

A Wavelet Based Image RetrievalKalyani Mali, Rana Datta Gupta . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 557

Table of Contents XXI

Use of Contourlets for Image RetrievalRajashekhar, Subhasis Chaudhuri . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 563

Integration of Keyword and Feature Based Search for Image RetrievalApplications

A. Vadivel, Shamik Sural, A.K. Majumdar . . . . . . . . . . . . . . . . . . . . . . . 570

Finding Locally and Periodically Frequent Sets and Periodic AssociationRules

A. Kakoti Mahanta, F.A. Mazarbhuiya, H.K. Baruah . . . . . . . . . . . . . 576

An Efficient Hybrid Hierarchical Agglomerative Clustering (HHAC)Technique for Partitioning Large Data Sets

P.A. Vijaya, M. Narasimha Murty, D.K. Subramanian . . . . . . . . . . . . 583

Density-Based View MaterializationA. Das, D.K. Bhattacharyya . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 589

On Simultaneous Selection of Prototypes and Features in Large DataT. Ravindra Babu, M. Narasimha Murty, V.K. Agrawal . . . . . . . . . . . 595

Knowledge Enhancement Through Ontology-Guided Text MiningMuhammad Abulaish, Lipika Dey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 601

Bioinformatics Application

A Novel Algorithm for Automatic Species Identification Using PrincipalComponent Analysis

Shreyas Sen, Seetharam Narasimhan, Amit Konar . . . . . . . . . . . . . . . . 605

Analyzing the Effect of Prior Knowledge in Genetic RegulatoryNetwork Inference

Gustavo Bastos, Katia S. Guimaraes . . . . . . . . . . . . . . . . . . . . . . . . . . . . 611

New Genetic Operators for Solving TSP: Application to MicroarrayGene Ordering

Shubhra Sankar Ray, Sanghamitra Bandyopadhyay,Sankar K. Pal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 617

Genetic Algorithm for Double Digest ProblemS. Sur-Kolay, S. Banerjee, S. Mukhopadhyaya, C.A. Murthy . . . . . . . 623

Intelligent Data Recognition of DNA Sequences Using Statistical ModelsJitimon Keinduangjun, Punpiti Piamsa-nga, Yong Poovorawan . . . . . 630

XXII Table of Contents

Parallel Sequence Alignment: A Lookahead ApproachPrasanta K. Jana, Nikesh Kumar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 636

Evolutionary Clustering Algorithm with Knowledge-Based Evaluationfor Fuzzy Cluster Analysis of Gene Expression Profiles

Han-Saem Park, Sung-Bae Cho . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 640

Biological Text Mining for Extraction of Proteins and TheirInteractions

Kiho Hong, Junhyung Park, Jihoon Yang, Sungyong Park . . . . . . . . . . 645

DNA Gene Expression Classification with Ensemble ClassifiersOptimized by Speciated Genetic Algorithm

Kyung-Joong Kim, Sung-Bae Cho . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 649

Web Intelligence and Genetic Algorithms

Multi-objective Optimization for Adaptive Web Site GenerationPrateek Jain, Pabitra Mitra . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 654

Speeding Up Web Access Using Weighted Association RulesAbhinav Srivastava, Abhijit Bhosale, Shamik Sural . . . . . . . . . . . . . . . . 660

An Automatic Approach to Classify Web Documents Using a DomainOntology

Mu-Hee Song, Soo-Yeon Lim, Seong-Bae Park, Dong-Jin Kang,Sang-Jo Lee . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 666

Distribution Based Stemmer RefinementB.L. Narayan, Sankar K. Pal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 672

Text Similarity Measurement Using Concept Representation ofTexts

Abhinay Pandya, Pushpak Bhattacharyya . . . . . . . . . . . . . . . . . . . . . . . . 678

Incorporating Distance Domination in Multiobjective EvolutionaryAlgorithm

Praveen K. Tripathi, Sanghamitra Bandyopadhyay,Sankar K. Pal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 684

I-EMO: An Interactive Evolutionary Multi-objective OptimizationTool

Kalyanmoy Deb, Shamik Chaudhuri . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 690

Table of Contents XXIII

Simultaneous Multiobjective Multiple Route Selection Using GeneticAlgorithm for Car Navigation

Basabi Chakraborty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 696

Rough Sets, Case-Based Reasoning and KnowledgeDiscovery

Outliers in Rough k-Means ClusteringGeorg Peters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 702

Divisible Rough Sets Based on Self-organizing MapsRocıo Martınez-Lopez, Miguel A. Sanz-Bobi . . . . . . . . . . . . . . . . . . . . . . 708

Parallel Island Model for Attribute ReductionMohammad M. Rahman, Dominik Slezak, Jakub Wroblewski . . . . . . . 714

On-Line Elimination of Non-relevant Parts of Complex Objects inBehavioral Pattern Identification

Jan G. Bazan, Andrzej Skowron . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 720

Rough Contraction Through Partial MeetsMohua Banerjee, Pankaj Singh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 726

Finding Interesting Rules Exploiting Rough MembershipsLipika Dey, Amir Ahmad, Sachin Kumar . . . . . . . . . . . . . . . . . . . . . . . . 732

Probability Measures for Prediction in Multi-table Infomation SystemsR.S. Milton, V. Uma Maheswari, Arul Siromoney . . . . . . . . . . . . . . . . . 738

Object Extraction in Gray-Scale Images by Optimizing RoughnessMeasure of a Fuzzy Set

D.V. Janardhan Rao, Mohua Banerjee, Pabitra Mitra . . . . . . . . . . . . . 744

Approximation Spaces in Machine Learning and Pattern RecognitionAndrzej Skowron, Jaros�law Stepaniuk, Roman Swiniarski . . . . . . . . . . 750

A Rough Set-Based Magnetic Resonance Imaging Partial VolumeDetection System

Sebastian Widz, Kenneth Revett, Dominik Slezak . . . . . . . . . . . . . . . . . 756

Eliciting Domain Knowledge in Handwritten Digit RecognitionTuan Trung Nguyen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 762

Collaborative Rough ClusteringSushmita Mitra, Haider Banka, Witold Pedrycz . . . . . . . . . . . . . . . . . . . 768

XXIV Table of Contents

Learning of General CasesSilke Janichen, Petra Perner . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 774

Learning Similarity Measure of Nominal Features in CBR ClassifiersYan Li, Simon Chi-Keung Shiu, Sankar Kumar Pal,James Nga-Kwok Liu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 780

Decision Tree Induction with CBRB. Radhika Selvamani, Deepak Khemani . . . . . . . . . . . . . . . . . . . . . . . . . 786

Rough Set Feature Selection Methods for Case-Based Categorization ofText Documents

Kalyan Moy Gupta, Philip G. Moore, David W. Aha,Sankar K. Pal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 792

An Efficient Parzen-Window Based Network Intrusion Detector Usinga Pattern Synthesis Technique

P. Viswanath, M. Narasimha Murty, Satish Kambala . . . . . . . . . . . . . . 799

Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 805