MEAN SHIFT FEATURE SPACE WARPING FOR RELEVANCE FEEDBACK

Relevance feedback has been taken as an essential tool to enhance content-based information retrieval systems by keeping the user in the retrieval loop. Among the fundamental relevance feedback approaches, feature space warping has been proposed as an effective approach for bridging the gap between high-level semantics and the low-level features. By examining the fundamental behavior of the feature space warping, we propose a new approach to harness its strength and resolve its weakness under various data distributions. Experiments on both synthetic data and real data reveal significant improvement from the proposed method.