Update Relevant Image Weights for Content-Based Image
Retrieval Using Support Vector Machines
Relevance feedback [cf.1] 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. 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 [cf.2] only the positive feedbacks, i.e., relevant images are
considered. In this paper, a novel approach is proposed by providing both positive and negative
feedbacks for Support Vector
Machines (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.
|