MUSE: A Content-Based Image Search and Retrieval System Using Relevance Feedback

被引：0
作者：
Oge Marques
Borko Furht
机构：
[1] Florida Atlantic University,Department of Computer Science and Engineering
来源：
Multimedia Tools and Applications | 2002年 / 17卷
关键词：
content-based image search and retrieval; relevance feedback; multimedia database systems; digital image processing;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
The field of Content-Based Visual Information Retrieval (CBVIR) has experienced tremendous growth in the recent years and many research groups are currently working on solutions to the problem of finding a desired image or video clip in a huge archive without resorting to metadata. This paper describes the ongoing development of a CBVIR system for image search and retrieval with relevance feedback capabilities. It supports browsing, query-by-example, and two different relevance feedback modes that allow users to refine their queries by indicating which images are good or bad at each iteration.
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页码：21 / 50
页数：29
相关论文
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