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We propose a new image retrieval system that provides users with both semantics based query and visual features based query. Our system has several advantages. First, it integrates visual features and semantics seamlessly. Second, it uses some effective techniques, such as image classification and relevance feedback, to bridge the gap between visual features and semantics. Third, it proposes several ways to obtain the semantic information of the image, which reduces manual labor and reduces the "subjectivity" of semantics by human. Fourth, it can update the semantics of the image by human intervention, which makes the image retrieval more flexible. We have implemented an image retrieval system based on our proposed approach. Experiments on an image database containing 22,000 items show that our scheme can achieve high efficiency.