DPF - a perceptual distance function for image retrieval
Baitao Li, Edward Yi Chang, Ching-Tung Wu · Proceedings - International Conference on Image Processing · 2003
For almost a decade, content-based image retrieval has been an active research area, yet one fundamental problem remains largely unsolved: how to measure perceptual similarity. To measure perceptual similarity, most researchers employ the Minkowski-type metric. Our extensive data-mining experiments on visual data show that, unfortunately, the Minkowski metric is not very effective in modeling perceptual similarity. Our experiments also show that the traditional "static" feature weighting approaches are not sufficient for retrieving various similar images. We report our discovery of a perceptual distance function through mining a large set of visual data. We call the discovered function dynamic the partial distance function (DPF). When we empirically compare the DPF to Minkowski-type distance functions, the DPF performs significantly better in finding similar images. The effectiveness of the DPF can be well explained by similarity theories in cognitive psychology.