Improvement of reuse of classifiers in CBIR using SVM active learning

Authors:
Masaaki Tekawa;Motonobu Hattori
Affiliations:
Interdisciplinary Graduate School of Medicine and Engineering, University of Yamanashi, Kofu, Yamanashi, Japan;Interdisciplinary Graduate School of Medicine and Engineering, University of Yamanashi, Kofu, Yamanashi, Japan
Venue:
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
Year:
2010

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Abstract

In content-based image retrieval, relevance feedback is often adopted as the method of interactions to grasp user's query concept. However, since this method tasks the user, a small amount of relevance feedback is desirable. For this purpose, Nakajima et al. have proposed a method in which classifiers learned by using relevance feedback are reused. In this paper, we improve the criterion for reuse of classifiers so that retrieval becomes more accurate and quick. Experimental results show that our method performs much better than the conventional methods.