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

作　　者：Amir H.Meghdadi James F.Peters 

机构地区：[1]Electrical and Computer Engineering,University of Manitoba,Winnipeg,Canada [2]School of Mathematics,University of Hyderabad,Hyderabad,India

出　　处：《International Journal of Intelligent Computing and Cybernetics》2012年第2期164-185,共22页智能计算与控制论国际期刊（英文）

基　　金：supported by the Natural Sciences and Engineering Research Council of Canada grant 185986.

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.

关 键 词：Image similarity Nearness measure Distance measurement Content-based image retrieval(CBIR) Perceptual tolerance neighbourhoods Tolerance spaces TOLERANCES Measurement 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]