Nearness of Subtly Different Digital Images

Puzio, Leszek; Peters, James F.

doi:10.1007/978-3-642-36505-8_5

Cited by 2 publications

(1 citation statement)

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“…Although the tolerance view of visual perception can be traced back to work by Poincaré (1985) on the similarity of sensations, this early work did not provide any practical application in modeling the visual perception of similarity in comparing digital images. Recent developments have demonstrated the efficacy of using tolerance relations in solving the image similarity problem (Peters and Ramanna, 2009;Peters, 2010aPeters, , b, 2009Meghdadi et al, 2009;Ramanna et al, , 2010Ramanna, 2011;Henry andPeters, 2009, 2010;Meghdadi and Peters, 2010;Wasilewski et al, 2010;Henry, 2010;Puzio and Peters, 2011).…”

Section: Introduction and Related Workmentioning

confidence: 99%

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

Meghdadi

Peters

2012

International Journal of Intelligent Computing and Cybernetics

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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.

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