Visual query processing for efficient image retrieval using a SOM-based filter-refinement scheme

Intelligent Database Systems Lab

Presenter : JHOU, YU-LIANG

Authors : ZHIWEN YU, HAU-SAN WONG,

JANE YOU, GUOQIANG HAN

2012,Information Sciences

Visual query processing for efficient image retrieval using

a SOM-based filter-refinement scheme


OutlinesMotivationObjectivesMethodologyExperimentsConclusionsComments


Motivation• Content-based image retrieval is one of the

most effective ways of retrieving visual data.

• However, When a large number of pictures

the relevance feedback process is a tedious

and time consuming and requires high

computational cost.


Objectives• To make the query process more robust.

• To make image retrieval more intuitive and effective.


Methodology-Framework


Methodologylocal membership function

(i) the neuron is in a d-level local neighborhood(ii) the distance between the weight vector of the neuron and the input vector is not greater than the s-th smallest distance among all the distances between the input vector and the weight vectors of the neurons in the neighborhood


MethodologyHierarchical tree


MethodologyCandidate retrieval


MethodologyVisualizing query processing


MethodologyVisualizing query processing


Experiments- dataset


Experimental setting

(ii) the feature vectors of the query images are randomly distributed is denoted as AVAC (r)

(i) the feature vectors of the query images are identical distributed is denoted as AVAC (i)


Experiments


Experiments


Experiments


Conclusions• We adopt our new approach can improve the

accuracy and efficiency of the process.


Comments• Advantages More accuracy and efficiency .Applications-Self organizing map -Image retrieval

Documents

Visual query processing for efficient image retrieval using a SOM-based filter-refinement scheme