Content-Based Image Retrieval： Near Tolerance Rough Set Approach

The problem considered in this paper is how to detect the degree of similarity in the content of digital images useful in image retrieval, i.e., to what extent is the content of a query image similar to content of other images. The solution to this problem results from the detection of subsets that are rough sets contained in covers of digital images determined by perceptual tolerance relations （PTRs）. Such relations are defined within the context of perceptual representative spaces that hearken back to work by J.I-I. Poincar6 on representative spaces as models of physical continua. Classes determined by a PTR provide content useful in content-based image retrieval （CBIR）. In addition, tolerance classes provide a means of determining when subsets of image covers are tolerance rough sets （TRSs）. It is the nearness of TRSs present in image tolerance spaces that provide a promising approach to CBIR, especially in cases such as satellite images or aircraft identification where there are subtle differences between pairs of digital images, making it difficult to quantify the similarities between such images. The contribution of this article is the introduction of the nearness of tolerance rough sets as an effective means of measuring digital image similarities and, as a significant consequence, successfully carrying out CBIR.