Summary

This project deals with the retrieval of visual content from large distributed on-line repositories such as image search engines. The wealth of visual content on the Web is rapidly growing. The extension of current visual retrieval techniques to a large-scale distributed environment such as the Web has encountered important technical barriers concerning heterogeneity, complexity and bandwidth on the Internet. We have developed a prototype content-based metasearch engine for images, MetaSEEk, to investigate the issues involved with efficiently querying large, distributed online image search engines for locating images of interest on the Web. The most recent version of MetaSEEk exploits user feedback in previous searches for recommending target search engines and integrating the results from different search engines in future queries.

This research is currently part of the IMKA project, which aims at developing intelligent multimedia knowledge applications.

People

Student Researchers:Ana B. Benitez, Mandis Beigi

Faculty: Prof. Shih-Fu Chang

Contact: Ana B. Benitez, Prof. Shih-Fu Chang

Publications

A. B. Benitez, M. Beige and S.-F. Chang, Using Relevance Feedback in Content-Based Image Metasearch, IEEE Internet Computing, Vol. 2, No. 4, pp. 59-69, Jul/Aug 1998.
(Paper: PDF)

M. Beigi, A. B. Benitez, and S.-F. Chang, MetaSEEk: A Content-Based Meta-Search Engine for Images, Proceedings of the SPIE 1998 Conference on Storage and Retrieval for Image and Video Databases VI (IST/SPIE-1998), Vol. 3312, San Jose, CA, Jan 28-30, 1998.
(Paper: PDF)

S.-F. Chang, J. R. Smith, M. Beigi and A. B. Benitez, Visual Information Retrieval from Large Distributed On-line Repositories, Communications of the ACM, Vol. 40, No. 12, pp. 63-71, Dec 1997.
(Paper: PDF)

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Last updated: July 9, 2003.