Abstract: Image similarity is often measured by computing the distance between two feature vectors. Unfortunately, the feature space cannot always capture the notion of similarity in human perception. Therefore, most current image retrieval systems use weights measuring the importance of each feature. In this paper new weight update rules are proposed for image retrieval purpose. In order to obtain optimal feature weights, database images are first divided into groups based on human perception, and then optimal feature weights for each database images are computed by using internal and outer query results. Experimental results show the proposed algorithm obtains more similar images to the query as the query process continues.
Hun-Woo Yoo , Sang-Sung Park and Dong-Sik Jang , 2004. Perception-based Feature Weight Refinement for Boosting Image Retrieval Performance . Asian Journal of Information Technology, 3: 1276-1283.