UCL logo

UCL Discovery

UCL home » Library Services » Electronic resources » UCL Discovery

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

Full text not available from this repository.

Abstract

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
Location: SANTA BARBARA, CA
Dates: 1998-06-23 - 1998-06-25
ISBN: 0-8186-8497-6
UCL classification: UCL > School of BEAMS > Faculty of Engineering Science > Computer Science
URI: http://discovery.ucl.ac.uk/id/eprint/153373
Downloads since deposit
0Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item