Large-scale image categorization is a challenging task. In this paper, we propose a new image categorization approach based on visual saliency and bag-of-words model. Firstly, a saliency map is generated by visual saliency method that exploits some coarsely localized information, i.e. the salient region shape and contour. Secondly, size of salient region is acquired by calculating maximum entropy. Thirdly, the local image descriptor-SIFT extracted in the salient region and visual saliency information are combined to build visual words. Finally, the visual word bag is categorized by Support Vector Machine. By comparing with BOW model categorization methods, experiment results show that our methods can effectively improve the accuracy of image categorization.