A Generalized Bayesian Learning Strategy for Relevance Feedback Region-Based Image Retrieval

This paper proposes a generalized Bayesian strategy for relevance feedback in Region-Based image retrieval The presented feedback technique is based on Bayesian learning method and incorporates a time-varying user model . We give the user model with two terms: a target query and a user conception. The user conception is aimed to learn a parameter set to determine the time-varying matching criterion. Therefore. at each feedback step, the learning process updates not only the target distribution but also the target query and the matching criterion. In addition, another objective of this paper is to conduct the relevance feedback on images represented in region level. Our experiments show that these learning methods are quite effective.