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Signal, Image and Video Processing

, Volume 2, Issue 3, pp 241–250 | Cite as

Feature selection for content-based image retrieval

  • Esin Guldogan
  • Moncef Gabbouj
Open Access
Original Paper

Abstract

In this article, we propose a novel system for feature selection, which is one of the key problems in content-based image indexing and retrieval as well as various other research fields such as pattern classification and genomic data analysis. The proposed system aims at enhancing semantic image retrieval results, decreasing retrieval process complexity, and improving the overall system usability for end-users of multimedia search engines. Three feature selection criteria and a decision method construct the feature selection system. Two novel feature selection criteria based on inner-cluster and intercluster relations are proposed in the article. A majority voting-based method is adapted for efficient selection of features and feature combinations. The performance of the proposed criteria is assessed over a large image database and a number of features, and is compared against competing techniques from the literature. Experiments show that the proposed feature selection system improves semantic performance results in image retrieval systems.

Keywords

Feature selection Mutual information Intercluster analysis Inner-cluster analysis Majority voting Content-based indexing and retrieval 

List of symbols

p(x)

Probability density functions

p(x, y)

Joint probability density function

I(X; Y)

Mutual information

H(X)

Shannon’s entropy

S

Correlation measure for evaluating the discrimination power of feature

c

Number of classes

δ

Correlation between clusters

fxi

ith item in the cluster x

μx

Mean of cluster x

σx

Standard deviation of cluster x

Nx

Cardinality of clusters x

e1

Eigen vector corresponding to the largest eigen value of the covariance matrix

π

The best representative feature vector

xN

Set of feature vectors

xi

Feature vector corresponding to the ith item in the cluster

xij

jth element of the feature vector corresponding to the ith item of the cluster

M

Mean vector

μj

Elements of M, mean values

Δ

Distance between π and M

n

Number of elements in the vectors π and M

d

Euclidean distance between cluster members

Sw1Sw2Sw3

Compactness measurements

r

Covering radius, distance from the center to the farthest item in the cluster

Π

Probability

υMI

Normalized numerical results from mutual information criterion

υICR

Normalized numerical results from inner-cluster relation criterion

υPPMC

Normalized numerical results from Pearson’s product-moment correlation criterion

\({\nu_{{f_{i}}}}\)

Votes for each feature

F

Number of features in the FSRL list

αi

Weights of the features in retrieval

Ri

Rank of the ith feature in FSRL list

ωi

Weight of item i in SPFL list

ωj

Weight of item j in FL list

Notes

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Copyright information

© The Author(s) 2008

Authors and Affiliations

  1. 1.Institute of Signal ProcessingTampere University of TechnologyTampereFinland

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