Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

4-2005

Abstract

Relevance feedback has been shown as an important tool to boost the retrieval performance in content-based image retrieval. In the past decade, various algorithms have been proposed to formulate relevance feedback in contentbased image retrieval. Traditional relevance feedback techniques mainly carry out the learning tasks by focusing lowlevel visual features of image content with little consideration on log information of user feedback. However, from a long-term learning perspective, the user feedback log is one of the most important resources to bridge the semantic gap problem in image retrieval. In this paper we propose a novel technique to integrate the log information of user feedback into relevance feedback for image retrieval. Our algorithm’s construction is based on a coupled support vector machine which learns consistently with the two types of information: the low-level image content and the user feedback log. We present a mathematical formulation of the problem and develop a practical algorithm to solve the problem effectively. Experimental results show that our proposed scheme is effective and promising.

Keywords

Support vector machines, Image retrieval, Content based retrieval, Information retrieval

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

ICDE '05: Proceedings of the 21st International Conference on Data Engineering Workshops, 3-4 April, Tokyo

First Page

1177

Last Page

1179

ISBN

9780769526577

Identifier

10.1109/ICDE.2005.233

Publisher

IEEE Computer Society

City or Country

Los Alamitos, CA

Additional URL

https://doi.org/10.1109/ICDE.2005.233

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