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

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.

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).

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.

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.