Relevance Feedback : A Power Tool for InteractiveContent - Based Image

| Content-Based Image Retrieval (CBIR) has become one of the most active research areas in the past few years. Many visual feature representations have been explored and many systems built. While these research eeorts establish the basis of CBIR, the usefulness of the proposed approaches is limited. Speciically, these eeorts have relatively ignored two distinct characteristics of CBIR systems: (1) the gap between high level concepts and low level features ; (2) subjectivity of human perception of visual content. This paper proposes a relevance feedback based interactive retrieval approach, which eeectively takes into account the above two characteristics in CBIR. During the retrieval process , the user's high level query and perception subjectivity are captured by dynamically updated weights based on the user's feedback. The experimental results over more than 70,000 images show that the proposed approach greatly reduces the user's eeort of composing a query and captures the user's information need more precisely.