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We present an image retrieval method based on the accumulated user relevance feedback records. Our method conducts the semi-supervised fuzzy clustering on the 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 evaluation and the corresponding query image of the records are used to predict the semantic correlation between the current retrieval query sample and the database images. Experiment results show that our method outperforms the traditional ones in both efficiency and effectiveness.