23 December 1997 Relevance feedback techniques in interactive content-based image retrieval
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Abstract
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 efforts establish the basis of CBIR, the usefulness of the proposed approaches is limited. Specifically, these efforts 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 effectively 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 relevance feedback. The experimental results show that the proposed approach greatly reduces the user's effort of composing a query and captures the user's information need more precisely.
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Yong Rui, Yong Rui, Thomas S. Huang, Thomas S. Huang, Sharad Mehrotra, Sharad Mehrotra, } "Relevance feedback techniques in interactive content-based image retrieval", Proc. SPIE 3312, Storage and Retrieval for Image and Video Databases VI, (23 December 1997); doi: 10.1117/12.298455; https://doi.org/10.1117/12.298455
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