文章基本信息

标题：Support Vector Machine Learning for Region-Based Image Retrieval with Relevance Feedback
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作者：Kim, Deok-Hwan ; Song, Jae-Won ; Lee, Ju-Hong 等
期刊名称：ETRI Journal
印刷版ISSN：1225-6463
电子版ISSN：2233-7326
出版年度：2007
卷号：29
期号：5
页码：700-702
语种：English
出版社：Electronics and Telecommunications Research Institute
摘要：We present a relevance feedback approach based on multi-class support vector machine (SVM) learning and cluster-merging which can significantly improve the retrieval performance in region-based image retrieval. Semantically relevant images may exhibit various visual characteristics and may be scattered in several classes in the feature space due to the semantic gap between low-level features and high-level semantics in the user's mind. To find the semantic classes through relevance feedback, the proposed method reduces the burden of completely re-clustering the classes at iterations and classifies multiple classes. Experimental results show that the proposed method is more effective and efficient than the two-class SVM and multi-class relevance feedback methods.
关键词：Support vector machine;cluster-merging;relevance feedback;region-based image retrieval