Relevance feedback based on parameter estimation of target distribution

Relevance feedback formulations have been proposed to refine query result in content-based image retrieval in the past few years. Many of them focus on a learning approach to solve the feedback problem. In this paper, we present an expectation maximization approach to estimate the user´s target distribution through user´s feedback. Furthermore, we describe how to use the maximum entropy display to fully utilize user´s feedback information. We detail the process and also demonstrate the result through experiments