Affinity relation discovery in image database clustering and content-based retrieval

M. Shyu, Shu‐Ching Chen, Min Chen, Chengcui Zhang
{"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.
图像数据库聚类和基于内容的检索中的亲和关系发现
在本文中,我们提出了一个统一的框架,称为马尔可夫模型中介(MMM),以方便图像数据库聚类和提高查询性能。MMM框架的结构包括两个层次:局部mm和集成mm,它们分别通过有效的数据挖掘过程对单个图像数据库和一组图像数据库中的图像之间的亲和关系进行建模。通过一组包含不同维度和概念类别的不同数量图像的图像数据库,验证了MMM框架在数据库聚类和图像检索方面的有效性和效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信