Abstract
Two methods for retrieval relevance optimization using the user’s feedback is proposed for a content-based image retrieval (CBIR) system. First, the feature space used in database image clustering for coarse classification is transferred to a preference feature space according to the user’s feedback by a map generated by supervised training, thereby enabling to collect user-preferred images in the matching candidates. Second, the parameters in the fine-matching relaxation operation is optimized according to the user’s evaluation of the retrieved image ranking using Particle Swarm Optimization. In the experiments, it is shown that the retrieval rankings are improved suiting the user’s preference when feature space mapping and parameter optimization are used.
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Okayama, M., Oka, N., Kameyama, K. (2008). Relevance Optimization in Image Database Using Feature Space Preference Mapping and Particle Swarm Optimization. In: Ishikawa, M., Doya, K., Miyamoto, H., Yamakawa, T. (eds) Neural Information Processing. ICONIP 2007. Lecture Notes in Computer Science, vol 4985. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69162-4_63
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DOI: https://doi.org/10.1007/978-3-540-69162-4_63
Publisher Name: Springer, Berlin, Heidelberg
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