Indexing for multipoint interactive similarity retrieval in iconic spatial image databases

Similarity-based retrieval of images is an important task in many image database applications. Interactive similarity retrieval is one way to resolve the fuzzy area involving psychological and physiological factors of individuals during the retrieval process. A good interactive similarity system depends not only on a good similarity measure, but also on the structure of the image database and the related retrieval process. In this paper, we propose to use a dynamic similarity measure on top of the enhanced digraph index structure for interactive iconic image similarity retrieval. Our approach makes use of the multiple feedbacks from the user to get the hidden subjective information of the retrieval, and avoids the high cost of re-computation of an interactive retrieval algorithm.