Perceptual tolerance neighborhood‐based similarity in content‐based image retrieval and classification
International Journal of Intelligent Computing and Cybernetics
ISSN: 1756-378X
Article publication date: 1 June 2012
Abstract
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.
Keywords
Citation
Meghdadi, A.H. and Peters, J.F. (2012), "Perceptual tolerance neighborhood‐based similarity in content‐based image retrieval and classification", International Journal of Intelligent Computing and Cybernetics, Vol. 5 No. 2, pp. 164-185. https://doi.org/10.1108/17563781211231525
Publisher
:Emerald Group Publishing Limited
Copyright © 2012, Emerald Group Publishing Limited