Active BSVM Learning for Relevance Feedback in Content-based Sketch Retrieval

S. Liang and Z. Sun (PRC)


Content-Based Sketch Retrieval (CBSR), Relevance Feedback, Active Learning, Biased SVM (BSVM)


The availability of relevance feedback is held back by the problem of the imbalance and limited size of labeled training data, as well as the real-time requirement of online interaction demands. In this paper, we propose a relevance feedback algorithm called active biased SVM (BSVM) learning, in which biased classification and active learning are employed to address these difficulties. The algorithm is applied to content-based sketch retrieval (CBSR), and the experiments prove both the effectiveness and efficiency of the proposed approach.

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