An Optimized Interaction Strategy for Bayesian Relevance Feedback

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 ? -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 ???????? ? , where ? ? is the size of the database, while a simple query-by-exampleapproach scales like ? ? , where depends on the structure of the database. This theoretical advantage is reflected by experiments with re...

• Type: Conference
• Year: 1998
• Where: CVPR
• Authors: Ingemar J. Cox, Matthew L. Miller, Thomas P. Minka, Peter N. Yianilos
• Keywords: Algorithm Takes Feedback, Categorical Relevance Judgments, Computer Vision, CVPR 1998, Item B, Relevance Feedback, Simple Query-by-exampleapproach Scales