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Semantic gap is the main problem in current content-based image retrieval. This paper proposes an approach which aims to learn semantic concepts from visual features. Each concept is modeled as a posterior pseudo-probability function, and the function parameters are trained from the positive and negative image examples of the concept using the max-min posterior pseudo-probabilities criterion. According to the posterior pseudo-probabilities of the query concept for all images, the image retrieval is realized by classifying all images into two categories: relevant to the query concept and irrelevant. The number of relevant images can be determined automatically. We show the effectiveness and the advantage of our approach through the experiments on Corel database.