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- W2020130929 abstract "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." @default.
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- W2020130929 date "2007-10-09" @default.
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- W2020130929 title "Support Vector Machine Learning for Region-Based Image Retrieval with Relevance Feedback" @default.
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- W2020130929 doi "https://doi.org/10.4218/etrij.07.0207.0037" @default.
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