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  1. Proceedings of the Tenth International Workshop on Multimedia Data Mining (MDMKDD '10)
  2. Relevance feature mapping for content-based image retrieval
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Relevance feature mapping for content-based image retrieval

Content Provider ACM Digital Library
Author Liu, Fei Tony Ting, Kai Ming Yin, Yilong Zhou, Guang-Tong
Abstract 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.
Starting Page 1
Ending Page 10
Page Count 10
File Format PDF
ISBN 9781450302203
DOI 10.1145/1814245.1814247
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2010-07-25
Publisher Place New York
Access Restriction Subscribed
Content Type Text
Resource Type Article
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