Incremental feature weight learning and its application to a shape-based query system

摘要

Similarity between shapes is often measured by computing the distance between two feature vectors. Unfortunately, the feature space cannot always capture the notion f similarity in human perception. So, most current image retrieval systems use weights measuring the importance of each feature. However, the similarity does not vary with equal strength or in the same proportion in all directions in the feature space. In this paper, we present feature weights based on both clustered objects in the database and on relevance feedback. We show that using variance information from shape clusters to guide cluster information for an initial database search gives better results than using the standard Euclidean distance.