Small sample size performance of evolutionary algorithms for adaptive image retrieval

Zoran Stejić*, Yasufumi Takama, Kaoru Hirota

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

1 Citation (Scopus)

Abstract

We evaluate the small sample size (SSS) performance of evolutionary algorithms (EAs) for relevance feedback (RP) in image retrieval. We focus on the requirement to learn the user's information need based on a small - between 2 and 25 - number of positive and negative training images. Despite this being a fundamental requirement, none of the existing works dealing with EAs for RF systematically evaluates their SSS performance. To address this issue, we compare four variants of EAs for RF. Common for all variants is the hierarchical, region-based image similarity model, with region and feature weights as parameters. The difference between the variants is in the objective function of the EA used to adjust the model parameters. The objective functions include: (O-l) precision; (O-2) average rank; (O-3) ratio of within-class (i.e., positive images) and between-class (i.e., positive and negative images) scatter; and (O-4) combination of O-2 and O-3. We note that unlike O-l and O-2 - O-3 and O-4 are not used in any of the existing works dealing with EAs for RF. The four variants are evaluated on five test databases, containing 61,895 general-purpose images, in 619 semantic categories. Results of the evaluation reveal that variants with objective functions O-3 and O-4 consistently outperform those with O-l and O-2. Furthermore, comparison with the representative of the existing RF methods shows that EAs are both effective and efficient approaches for SSS learning in region-based image retrieval.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsPeter Enser, Yiannis Kompatsiaris, Noel E. O’Connor, Alan F. Smeaton, Arnold W. M. Smeulders
PublisherSpringer Verlag
Pages51-59
Number of pages9
ISBN (Print)3540225390, 9783540225393
DOIs
Publication statusPublished - 2004
Externally publishedYes

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3115
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

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