Optimized interaction strategy for Bayesian relevance feedback.
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
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. 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.
|Title:||Optimized interaction strategy for Bayesian relevance feedback|
|UCL classification:||UCL > School of BEAMS
UCL > School of BEAMS > Faculty of Engineering Science
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