RICH GET RICHER—AN ADAPTIVE RELEVANCE FEEDBACK APPROACH FOR CONTENT BASED IMAGE RETRIEVAL

Content based image retrieval（CBIR） has become one of the most active research areas in the past few years. At the early stage of CBIR, research primarily focused on exploring various feature representations, and ignored the subjectivity of human perception. There exists a gap between high level concepts and low level features. Relevance feedback is a promising approach to finding a mapping between semantic objects and low level features. A novel relevance feedback approach for image retrieval, the Rich Get Richer （RGR） strategy, is proposed in this paper. It is based on the general framework of Bayesian inference in statistics. The user’s feedback information is propagated into the retrieval process step by step. With the Rich Get Richer （RGR） strategy, the more promising images are emphasized. On the contrary, the less promising ones are de emphasized. The experimental results show that the proposed approach greatly reduces the user’s efforts of composing a query, and captures the needed information for the user more precisely.