Abstract
This paper presents a composite image retrieval approach based on the analysis of the accumulated user relevance feedback records. To improve efficiency, semi-supervised fuzzy clustering is employed to classify the RF records, and the subsequent information filtering within the target cluster is performed to guide the refinement of query parameters. During information filtering, both the user’s relevance evaluations and the corresponding query images of the records are used to predict the semantic correlation between the database images and the current retrieval. Experiment results show that our method outperforms the traditional ones in both efficiency and effectiveness.
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Zhou, X., Zhang, Q., Liu, L., Deng, A., Zhang, L., Shi, B. (2003). An Image Retrieval Method Based on Information Filtering of User Relevance Feedback Records. In: Dong, G., Tang, C., Wang, W. (eds) Advances in Web-Age Information Management. WAIM 2003. Lecture Notes in Computer Science, vol 2762. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45160-0_42
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DOI: https://doi.org/10.1007/978-3-540-45160-0_42
Publisher Name: Springer, Berlin, Heidelberg
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