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In this paper, we introduce an efficient content-based image retrieval system based on fuzzy relevance feedback. Conventional content based image retrieval (CBIR) systems that use relevance feedback (RF), want user to mark retrieved images as relevant or irrelevant, while this determination is difficult for images which are rich in semantic. As a result, this system integrates the log information of user feedback using a soft feedback model to construct fuzzy transaction repository (FTR). The repository remembers the userpsilas intent and therefore, provides a better representation of each image in the database. The semantic similarity between the query image and each database image can then be computed using the current feedback and the semantic values in the FTR. Furthermore, the SVM is applied to the session-term feedback in order to learn the visual similarity. These two similarity measures are normalized and combined together to form the overall similarity measure. Experimental results using a COREL database demonstrate the effectiveness of the proposed method.