How to detect visual salient regions is a challenging problem in computer vision. Recently, saliency detection methods that use boundaries or convex hulls under Bayesian framework have attracted lots of attention. Although these methods achieve state-of-the-art results, there still exist some limitations, e.g., the background will get highlighted when the initial convex hulls are not good enough. This paper presents a new algorithm that retains the advantages of such saliency maps while overcoming their shortcomings. First, the initial convex hull is improved by the image matting model which can be efficiently solved by an edge-preserving filter. Second, a more accurate prior map can be obtained by the improved convex hull. Third, the final convex hull is further refined by an edge-preserving filter to compute the observation likelihood. Finally, the Bayesian framework is employed to compute the saliency map. Extensive experiments compared with state-of-the-art saliency detection algorithms demonstrate the effectiveness of our method.