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Erschienen in:
2010 | OriginalPaper | Buchkapitel
Improvement of Reuse of Classifiers in CBIR Using SVM Active Learning
verfasst von : Masaaki Tekawa, Motonobu Hattori
Erschienen in: Neural Information Processing. Models and Applications
Verlag: Springer Berlin Heidelberg
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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.