Abstract: Traditional Bayesian model saliency detection algorithm may have a poor performance in terms of precision.Therefore,this paper proposed a novel algorithm based on the multi-scaled convex hull.Firstly,the manifold ranking (MR) algorithm is used to extract the foreground of the images in the CIELab color space,which is considered as the prior probability map.Secondly,the image is down sampled by Gaussian Pyramid algorithm,and three scaled images are obtained.The improved convex hull is derived by using the intersection about convex hull of Harris corners of the three scaled images.Thirdly,the color histogram and convex hull are combined to calculate the observation likelihood probability.Finally,according to the existing prior probability map and observation likelihood probability,the Bayesian model is used to compute the saliency map.Moreover,the optimization is carried out for better performance.The experiment results on public datasets MSRA1000 and ECSSD show that the proposedalgorithm not only achieves good vision effect,but also improves the performance evaluation of precision-recall curves and F-measure value.

Key words: Bayesian model, Convex hull, Manifold Ranking algorithm, precision-recall curves, Saliency detection