In this paper, we propose a probabilistic model for web image mining, which is based on concept-sensitive salient regions without human intervene. Our goal is to achieve a middle-level understanding of image semantics to bridge the semantic gap existing in the field of image mining and retrieval. With the help of a popular search engine, semantically relevant images are collected, and concept-sensitive salient regions are extracted automatically based on an attention model. Then the semantic concept model is learned from the joint distribution of all salient regions with Gaussian Mixture Model and Expectation-Maximization algorithm. In addition, by incorporating semantically irrelevant un-salient regions as negative samples, the discriminative power of the solution is further enhanced. Experiments demonstrate the encouraging performance of the proposed method.