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This paper presents a retrieval pattern-based inter-query learning approach for image retrieval with relevance feedback. The proposed system combines SVM-based low-level learning and semantic correlation-based high-level learning to construct a semantic matrix to store retrieval patterns of a certain number of randomly chosen query sessions. User's relevance feedback is utilized for updating high-level semantic features of the query image and each database image. Extensive experiments demonstrate our system outperforms three peer systems in the context of both correct and erroneous feedback. Our retrieval system also achieves high retrieval accuracy after the first iteration.