Incorporate discriminant analysis with EM algorithm in image retrieval

Q. Tian*, Y. Wu, T. S. Huang

*Corresponding author for this work

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

10 Scopus citations


One of the difficulties of Content-Based Image Retrieval (CBIR) is the gap between high-level concepts and low-level image features, e.g., color and texture. Relevance feedback was proposed to take into account of the above characteristics in CBIR. Although relevance feedback incrementally supplies more information for fine retrieval, two challenges exist: (1) the labeled images from the relevance feedback are still very limited compared to the large unlabeled images in the image database. (2) relevance feedback does not offer a specific technique to automatically weight the low-level feature. In this paper, image retrieval is formulated as a transductive learning problem by combining unlabeled images in supervised learning to achieve better classification. Experimental results show that the proposed approach has a satisfactory performance for image retrieval applications.

Original languageEnglish (US)
Title of host publicationIEEE International Conference on Multi-Media and Expo
Number of pages4
StatePublished - Dec 1 2000
Event2000 IEEE Internatinal Conference on Multimedia and Expo (ICME 2000) - New York, NY, United States
Duration: Jul 30 2000Aug 2 2000


Other2000 IEEE Internatinal Conference on Multimedia and Expo (ICME 2000)
Country/TerritoryUnited States
CityNew York, NY

ASJC Scopus subject areas

  • General Engineering


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