Improving perceptual matching in color image retrieval

All researchers in CBIR (Content-Based Image Retrieval) domain try hard to match computer calculated distance with the distance sensed by human visual system. But, in our opinion, the present status is still far from achieving satisfactory matching. We aim to present a method that we hope can make some progress towards satisfactory matching. In the full paper, our method is explained in much detail; here we give only a briefing. We discuss the following two topics: (1) the shortcoming of Minkowski distance; (2) reducing the inadequacy of Minkowski distance with a dynamic perception-based distance based on Weber-Fechner Rule. Finally we present experimental results for two feature (color histogram) databases from a relatively large image database. We retrieved color images by our method and also by two Minkowski methods; L₁ and L₂. Here we give only the comparison results of our method with L₁ method (better than L₂): for one feature database, the retrieval efficiency of our new method is better than that of L₁ by 6.6% to 24.8%, the average being 16.7%; for the other feature database, the retrieval efficiency of our method is better than that of L₁ by 6.2% to 22.7%, the average being 9.7%.