Multiple object retrieval for image databases using multiple instance learning and relevance feedback

Chengcui Zhang, Shu Ching Chen, Mei Ling Shyu

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

15 Scopus citations

Abstract

This paper proposes a method to effectively discover users' concept patterns when multiple objects of interests (e.g., foreground and background objects) are involved in content-based image retrieval. The proposed method incorporates Multiple Instance Learning into the user relevance feedback in a seamless way to discover where the user's most interested objects/regions and how to map the local features of that region(s) to user's high-level concepts. A three-layer neural network is used to model the underlying mapping progressively through the feedback and learning procedure.

Original languageEnglish (US)
Title of host publication2004 IEEE International Conference on Multimedia and Expo (ICME)
Pages775-778
Number of pages4
StatePublished - Dec 1 2004
Event2004 IEEE International Conference on Multimedia and Expo (ICME) - Taipei, Taiwan, Province of China
Duration: Jun 27 2004Jun 30 2004

Publication series

Name2004 IEEE International Conference on Multimedia and Expo (ICME)
Volume2

Other

Other2004 IEEE International Conference on Multimedia and Expo (ICME)
CountryTaiwan, Province of China
CityTaipei
Period6/27/046/30/04

ASJC Scopus subject areas

  • Engineering(all)

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  • Cite this

    Zhang, C., Chen, S. C., & Shyu, M. L. (2004). Multiple object retrieval for image databases using multiple instance learning and relevance feedback. In 2004 IEEE International Conference on Multimedia and Expo (ICME) (pp. 775-778). (2004 IEEE International Conference on Multimedia and Expo (ICME); Vol. 2).