DPF - a perceptual distance function for image retrieval

Abstract

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. In this paper, we report our discovery of a perceptual distance function through mining a large set of visual data. We call the discovered function dynamic partial distance function (DPF). When we empirically compare DPF to Minkowskitype distance functions, DPF performs significantly better in finding similar images. The effectiveness of DPF can be well explained by similarity theories in cognitive psychology.

DOI: 10.1109/ICIP.2002.1040021

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@inproceedings{Chang2002DPFA, title={DPF - a perceptual distance function for image retrieval}, author={Baitao Li Chang and E. Ching-Tung Wu}, booktitle={ICIP}, year={2002} }