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.H.Poincare 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.

Perceptual tolerance neighborhood-based similarity in content-based image retrieval and classification

Purpose–The purpose of this paper is to demonstrate the effectiveness and advantages of using perceptual tolerance neighbourhoods in tolerance space-based image similarity measures and its application in content-based image classification and retrieval.Design/methodology/approach–The proposed method in this paper is based on a set-theoretic approach,where an image is viewed as a set of local visual elements.The method also includes a tolerance relation that detects the similarity between pairs of elements,if the difference between corresponding feature vectors is less than a threshold 2(0,1).Findings–It is shown that tolerance space-based methods can be successfully used in a complete content-based image retrieval(CBIR)system.Also,it is shown that perceptual tolerance neighbourhoods can replace tolerance classes in CBIR,resulting in more accuracy and less computations.Originality/value–The main contribution of this paper is the introduction of perceptual tolerance neighbourhoods instead of tolerance classes in a new form of the Henry-Peters tolerance-based nearness measure(tNM)and a new neighbourhood-based tolerance-covering nearness measure(tcNM).Moreover,this paper presents a side–by–side comparison of the tolerance space based methods with other published methods on a test dataset of images.