On learning perceptual distance function for image retrieval

For almost a decade, Content-Based Information Retrieval has been an active research area, yet two fundamental problems remain largely unsolved: how best to learn users´ query concepts, and how to measure perceptual similarity. To learn subjective query concepts, most systems use relevance feedback techniques. However, these traditional techniques often require a large number of training instances to converge to a concept, and a typical online user may be too impatient to provide much feedback. Thus traditional relevance feedback techniques are ineffective. To measure perceptual similarity, most researchers employ the Minkowski metric or the L-norm distance function. Our extensive data-mining experiments on visual data show that, unfortunately, the Minkowski-type metric is ineffective in modeling perceptual similarity. In this paper, we report the progress we have made recently in developing more effective methods for learning and measuring perceptual similarity, and our future research plans.