{"title":"Affinity relation discovery in image database clustering and content-based retrieval","authors":"M. Shyu, Shu‐Ching Chen, Min Chen, Chengcui Zhang","doi":"10.1145/1027527.1027614","DOIUrl":null,"url":null,"abstract":"In this paper, we propose a unified framework, called <i>Markov Model Mediator</i> (MMM), to facilitate image database clustering and to improve the query performance. The structure of the MMM framework consists of two hierarchical levels: local MMMs and integrated MMMs, which model the affinity relations among the images within a single image database and within a set of image databases, respectively, via an effective data mining process. The effectiveness and efficiency of the MMM framework for database clustering and image retrieval are demonstrated over a set of image databases which contain various numbers of images with different dimensions and concept categories.","PeriodicalId":292207,"journal":{"name":"MULTIMEDIA '04","volume":"129 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2004-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"18","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"MULTIMEDIA '04","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/1027527.1027614","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 18
Abstract
In this paper, we propose a unified framework, called Markov Model Mediator (MMM), to facilitate image database clustering and to improve the query performance. The structure of the MMM framework consists of two hierarchical levels: local MMMs and integrated MMMs, which model the affinity relations among the images within a single image database and within a set of image databases, respectively, via an effective data mining process. The effectiveness and efficiency of the MMM framework for database clustering and image retrieval are demonstrated over a set of image databases which contain various numbers of images with different dimensions and concept categories.