Ana B. Benitez, Mandis Beigi and Shih-Fu Chang. Using Relevance Feedback in Content-Based Image Metasearch. IEEE Internet Computing, 2(4):59-69, July 1998.
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The proliferation of online documents and search engines in the past few years has motivated the development of metasearch engines, which act as gateways that link users automatically and transpar-ently to multiple and maybe competing search engines. Current metasearch engines use a variety of techniques, though all of them work with the fundamental searching unit of text. Automated visual information retrieval (VIR) systems work with image features such as colors, textures, and shapes in combination with text and other related information to query the increasing store of image data available on the Web. New VIR systems pose special problems of heterogeneity and performance that motivate the development of metasearch engines in this domain. We have developed a prototype content-based metasearch engine for images, called MetaSeek, to investigate the issues involved with effi-ciently querying large, distributed online visual information sources. MetaSeek selects and queries the target image search engines according to their success under similar query conditions in previous searches. The current implementation keeps track of each target engine’s perfor-mance by integrating user feedback for each visual query into a perfor-mance database. We begin this article with a review of the issues in content-based visual query, then describe the current MetaSeek implementation. We present the results of experiments that evaluated the implementation in comparison to a previous version of the system and a baseline engine that ran-domly selects the individual search engines to query. We conclude by sum-marizing open issues for future research
@article{dvmmPub114,
Author = {Benitez, Ana B. and Beigi and Shih-Fu Chang, Mandis},
Title = {Using Relevance Feedback in Content-Based Image Metasearch},
Journal = {IEEE Internet Computing},
Volume = {2},
Number = {4},
Pages = {59--69},
Month = {July},
Year = {1998}
}
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