A Multiple Instance Learning Approach for Content Based Image Retrieval Using One-Class Support Vector Machine

Chengcui Zhang; Xin Chen; Min Chen; Shu-Ching Chen; Mei-Ling Shyu

doi:10.1109/ICME.2005.1521628

2005 IEEE International Conference on Multimedia and Expo

A Multiple Instance Learning Approach for Content Based Image Retrieval Using One-Class Support Vector Machine

Year: 2005, Pages: 1142-1145

DOI Bookmark: 10.1109/ICME.2005.1521628

Authors

Chengcui Zhang, Department of Computer and Information Sciences, University of Alabama at Birmingham
Xin Chen
Min Chen
Shu-Ching Chen
Mei-Ling Shyu

Abstract

Multiple Instance Learning (MIL) is a special kind of supervised learning problem that has been studied actively in recent years. In this paper, we propose an approach based on One-Class Support Vector Machine (SVM) to solve MIL problem in the region-based Content Based Image Retrieval (CBIR). Relevance Feedback technique is incorporated to provide progressive guidance to the learning process. Performance is evaluated and the effectiveness of our retrieval algorithm has been shown through comparative studies.

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A Multiple Instance Learning Approach for Content Based Image Retrieval Using One-Class Support Vector Machine

Authors

Abstract

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