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

摘要

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.