Image retrieval based on similarity score fusion from feature similarity ranking lists

Authors:
Mladen Jović;Yutaka Hatakeyama;Fangyan Dong;Kaoru Hirota
Affiliations:
Dept. of Computational Intelligence and Systems Science, Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology, Yokohama, Japan;Dept. of Computational Intelligence and Systems Science, Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology, Yokohama, Japan;Dept. of Computational Intelligence and Systems Science, Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology, Yokohama, Japan;Dept. of Computational Intelligence and Systems Science, Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology, Yokohama, Japan
Venue:
FSKD'06 Proceedings of the Third international conference on Fuzzy Systems and Knowledge Discovery
Year:
2006

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Abstract

An image similarity method based on the fusion of similarity scores of feature similarity ranking lists is proposed. It takes an advantage of combining the similarity value scores of all feature types representing the image content by means of different integration algorithms when computing the image similarity. Three fusion algorithms for the purpose of fusing image feature similarity scores from the feature similarity ranking lists are proposed. Image retrieval experimental results of the evaluation on four general purpose image databases with 4,444 images classified into 150 semantic categories reveal that a proposed method results in the best overall retrieval performance in comparison to the methods employing single feature similarity lists when determining image similarity with an average retrieval precision higher about 15%. Compared to two well-known image retrieval system, SIMPLicity and WBIIS, the proposed method brings an increase of 4% and 27% respectively in average retrieval precision. The proposed method based on multiple criteria thus provides better approximation of the user's similarity criteria when modeling image similarity.