标题:Relevance feature mapping for content-based image retrieval
作者:Zhou, Guang-Tong ;Ting, Kai Ming ;Liu, Fei Tony ;Yin, Yilong
通讯作者:Zhou, GT
作者机构:[Zhou, Guang-Tong ;Yin, Yilong ] School of Computer Science and Technology, Shandong University, Jinan 250101, China;[Ting, Kai Ming ;Liu, Fei Tony ] 更多
会议名称:10th International Workshop on Multimedia Data Mining, MDMKDD '10
会议日期:25 July 2010 through 25 July 2010
来源:Proceedings of the 10th International Workshop on Multimedia Data Mining, MDMKDD '10
出版年:2010
DOI:10.1145/1814245.1814247
关键词:Algorithms
摘要:This paper presents a ranking framework for content-based image retrieval using relevance feature mapping. Each relevance feature measures the relevance of an image to some profile underlying the image database. The framework is a two-stage process. In the off-line modeling stage, it constructs a collection of models which maps all images in the database to the relevance feature space. In the on-line retrieval stage, it assigns a weight to every relevance feature based on the query image, and then ranks images in the database according to their weighted average feature values. The framework also incorporates relevance feedback which modifies the ranking based on the feedbacks through reweighted features. We show that the power of the proposed framework is coming from the relevance features. Experiments on a large image database validate the efficacy and efficiency of the proposed framework. © 2010 ACM.
收录类别:EI;SCOPUS
Scopus被引频次:3
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-77956247974&doi=10.1145%2f1814245.1814247&partnerID=40&md5=0e9ceadfbffa558fd3a19ec02889fe5e
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