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While providing relevance feedback (RF) by users proves to be an effective method for content-based image retrieval, how to interpret and learn from the user-provided feedback, however, remains an unsolved problem. In this paper, we propose an integrated users-feedback and learning algorithm by screening individual elements of content features and driving a group of swarmed particles inside the feature...
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