IEEE Transactions on Circuits and Systems for Video Technology, 1998
Based Image Retrieval CBIR has become one of the most active research areas in the past few years... more 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 e orts establish the basis of CBIR, the usefulness of the proposed approaches is limited. Speci cally, these e orts have relatively ignored two distinct characteristics of CBIR systems: 1 the gap between high level concepts and low level features; 2 subjectivity o f h uman perception of visual content. This paper proposes a relevance feedback based interactive retrieval approach, which e ectively takes into account the above t wo c haracteristics 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 e ort of composing a query and captures the user's information need more precisely. Keywords| Content-Based Image Retrieval, interactive multimedia processing, relevance feedback
IEEE Transactions on Circuits and Systems for Video Technology, 1998
Based Image Retrieval CBIR has become one of the most active research areas in the past few years... more 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 e orts establish the basis of CBIR, the usefulness of the proposed approaches is limited. Speci cally, these e orts have relatively ignored two distinct characteristics of CBIR systems: 1 the gap between high level concepts and low level features; 2 subjectivity o f h uman perception of visual content. This paper proposes a relevance feedback based interactive retrieval approach, which e ectively takes into account the above t wo c haracteristics 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 e ort of composing a query and captures the user's information need more precisely. Keywords| Content-Based Image Retrieval, interactive multimedia processing, relevance feedback
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