Incorporate support vector machines to content-based image retrieval with relevance feedback

Abstract

By using relevance feedback, content-based image retrieval (CBIR) allows the user to retrieve images interactively. Beginning with a coarse query, the user can select the most relevant images and provide a weight of preference for each relevant image to refine the query. The high level concept borne by the user and perception subjectivity of the user can be automatically captured by the system to some degree. This paper proposes an approach to utilize both positive and negative feedbacks for image retrieval. Support vector machines (SVM) is applied to classifying the positive and negative images. The SVM learning results are used to update the preference weights for the relevant images. This approach releases the user from manually providing preference weight for each positive example. Experimental results show that the proposed approach has improvement over the previous approach (Rui et al. 1997) that uses positive examples only.