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Proceedings Paper

MetaSEEk: a content-based metasearch engine for images
Author(s): Mandis Beigi; Ana Belen Benitez; Shih-Fu Chang
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Paper Abstract

Search engines are the most powerful resources for finding information on the rapidly expanding World Wide Web (WWW). Finding the desired search engines and learning how to use them, however, can be very time consuming. The integration of such search tools enables the users to access information across the world in a transparent and efficient manner. These systems are called meta-search engines. The recent emergence of visual information retrieval (VIR) search engines on the web is leading to the same efficiency problem. This paper describes and evaluates MetaSEEk, a content-based meta-search engine used for finding images on the Web based on their visual information. MetaSEEk is designed to intelligently select and interface with multiple on-line image search engines by ranking their performance for different classes of user queries. User feedback is also integrated in the ranking refinement. We compare MetaSEEk with a base line version of meta-search engine, which does not use the past performance of the different search engines in recommending target search engines for future queries.

Paper Details

Date Published: 23 December 1997
PDF: 11 pages
Proc. SPIE 3312, Storage and Retrieval for Image and Video Databases VI, (23 December 1997); doi: 10.1117/12.298436
Show Author Affiliations
Mandis Beigi, Columbia Univ. (United States)
Ana Belen Benitez, Columbia Univ. (United States)
Shih-Fu Chang, Columbia Univ. (United States)


Published in SPIE Proceedings Vol. 3312:
Storage and Retrieval for Image and Video Databases VI
Ishwar K. Sethi; Ramesh C. Jain, Editor(s)

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