Abstract
To utilize users’ relevance feedback is a significant and challenging issue in content-based image retrieval due to its capability of narrowing the “semantic gap” between the low-level features and the higher-level concepts. This paper proposes a novel relevance feedback framework for image retrieval based on Ant Colony algorithm, by accumulating users’ feedback to construct a “hidden” semantic network and achieve a “memory learning” mechanism in image retrieval process. The proposed relevance feedback framework adopts both the generated semantic network and the extracted image features, and then re-weights them in similarity calculation to obtain more accurate retrieval results. Experimental results and comparisons are illustrated to demonstrate the effectiveness of the proposed framework.
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Chen, GP., Yang, YB., Zhang, Y., Pan, LY., Gao, Y., Shang, L. (2011). A Relevance Feedback Framework for Image Retrieval Based on Ant Colony Algorithm . In: Bebis, G., et al. Advances in Visual Computing. ISVC 2011. Lecture Notes in Computer Science, vol 6939. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24031-7_33
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DOI: https://doi.org/10.1007/978-3-642-24031-7_33
Publisher Name: Springer, Berlin, Heidelberg
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