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

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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 and Mei-Ling Shyu

2005 IEEE International Conference on Multimedia and Expo, Vol.2005, pp.1142-1145

2005

DOI: https://doi.org/10.1109/ICME.2005.1521628

Abstract

Computer science

Content based retrieval

Feedback

Image retrieval

Image segmentation

Information retrieval

Machine learning

Radio frequency

Supervised learning

Support vector machines

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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Title: A Multiple Instance Learning Approach for Content Based Image Retrieval Using One-Class Support Vector Machine
Creators: Chengcui Zhang - Department of Computer and Information Sciences, University of Alabama at Birmingham
Xin Chen
Min Chen
Shu-Ching Chen
Mei-Ling Shyu
Publication Details: 2005 IEEE International Conference on Multimedia and Expo, Vol.2005, pp.1142-1145
Publisher: IEEE
Academic Unit: College of Engineering; CoE - Electrical & Computer Engineering
Language: English
Resource Type: Conference proceeding
Record Identifier: 991031620087102976