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Target testing and the PicHunter Bayesian multimedia retrieval system

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4 Author(s)
Cox, Ingemar J. ; NEC Res. Inst., Princeton, NJ, USA ; Miller, M.L. ; Omohundro, S.M. ; Yianilos, P.N.

We address how the effectiveness of a content-based, multimedia information retrieval system can be measured, and how such a system should best use response feedback in performing searches. We propose a simple, quantifiable measure of an image retrieval system's effectiveness, “target testing”, in which effectiveness is measured as the average number of images that a user must examine in searching for a given random target. We describe an initial version of PicHunter, a retrieval system designed to test a novel approach to relevance-feedback. This approach is based on a Bayesian framework that incorporates an explicit model of the user's selection process. PicHunter is intentionally designed to have a minimal, “queryless” user interface, so that its performance reflects only the performance of the relevance feedback algorithm. The algorithm, however, can easily be incorporated into more traditional, query-based systems. Employing no explicit query, and only a small amount of image processing, PicHunter is able to locate randomly selected targets in a database of 4522 images after displaying an average of only 55 groups of 4 images. This is more than 10 times better than random chance

Published in:

Digital Libraries, 1996. ADL '96., Proceedings of the Third Forum on Research and Technology Advances in

Date of Conference:

13-15, May 1996

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