ETRI Journal
- 제29권5호
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- Pages.700-702
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- 2007
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- 1225-6463(pISSN)
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- 2233-7326(eISSN)
Support Vector Machine Learning for Region-Based Image Retrieval with Relevance Feedback
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Kim, Deok-Hwan
(School of Electrical Engineering, Inha University) ;
- Song, Jae-Won (School of Computer Science and Engineering, Inha University) ;
- Lee, Ju-Hong (School of Computer Science and Engineering, Inha University) ;
- Choi, Bum-Ghi (School of Computer Science and Engineering, Inha University)
- 투고 : 2007.02.23
- 발행 : 2007.10.31
초록
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.