Update relevant image weights for content-based image retrieval using support vector machines

Relevance feedback (Y. Rui et al., 1998) has been a powerful tool for interactive content based image retrieval (CBIR). During the retrieval process, the user selects the most relevant images and provides a weight of preference for each relevant image. The user's high level query and perception subjectivity can be captured to some extent by dynamically updated low-level feature weights based on the user's feedback. However, in MARS (Y. Rui et al., 1997), only the positive feedbacks, i.e., relevant images are considered. A novel approach is proposed by providing both positive and negative feedbacks for support vector machine (SVM) learning. The SVM learning results are used to update the weights of preference for relevant images. Priorities are given to the positive feedbacks that have larger distances to the hyperplane determined by the support vectors. This approach releases the user from manually providing preference weight for each positive example, i.e., relevant image as before. Experimental results show that the proposed approach has reasonable improvement over relevance feedback with possible examples only.