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An optimized interaction strategy for Bayesian relevance feedback

Cox, IJ; Miller, ML; Minka, TP; Yianilos, PN; (1998) An optimized interaction strategy for Bayesian relevance feedback. In: 1998 IEEE COMPUTER SOCIETY CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, PROCEEDINGS. (pp. 553 - 558). IEEE COMPUTER SOC

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A new algorithm and systematic evaluation is presented for searching a database via relevance feedback. It represents a new image display strategy for the PicHunter system [2, 1]. The algorithm takes feedback in the form of relative judgments ("item A is more relevant than item B") as opposed to the stronger assumption of categorical relevance judgments ("item A is relevant but item B is not"). It also exploits a learned probabilistic model of human behavior to make better use of the feedback it obtains. The algorithm can be viewed as an extension of indexing schemes like the k-d tree to a stochastic setting, hence the name "stochastic-comparison search." In simulations, the amount of feedback required for the new algorithm scales like log(2) \D\, where \D\ is the size of the database, while a simple query-by-example approach scales like \D\(a), where a < 1 depends on the structure of the database. This theoretical advantage is reflected by experiments with real users on a database of 1500 stock photographs.

Type: Proceedings paper
Title: An optimized interaction strategy for Bayesian relevance feedback
Event: 1998 IEEE Computer-Society Conference on Computer Vision and Pattern Recognition
Dates: 1998-06-23 - 1998-06-25
ISBN: 0-8186-8497-6
URI: http://discovery.ucl.ac.uk/id/eprint/153373
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