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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

31 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationIEEE International Conference on Multimedia and Expo, ICME 2005
Pages1142-1145
Number of pages4
DOIs
StatePublished - Dec 1 2005
EventIEEE International Conference on Multimedia and Expo, ICME 2005 - Amsterdam, Netherlands
Duration: Jul 6 2005Jul 8 2005

Publication series

NameIEEE International Conference on Multimedia and Expo, ICME 2005
Volume2005

Other

OtherIEEE International Conference on Multimedia and Expo, ICME 2005
CountryNetherlands
CityAmsterdam
Period7/6/057/8/05

ASJC Scopus subject areas

  • Engineering(all)

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    Zhang, C., Chen, X., Chen, M., Chen, S. C., & Shyu, M. L. (2005). A multiple instance learning approach for content based image retrieval using one-class support vector machine. In IEEE International Conference on Multimedia and Expo, ICME 2005 (pp. 1142-1145). [1521628] (IEEE International Conference on Multimedia and Expo, ICME 2005; Vol. 2005). https://doi.org/10.1109/ICME.2005.1521628