Learning Optimal Seeds for Salient Object Detection

Visual saliency detection is useful for applications as object recognition, resizing and image segmentation. It is a challenge to detect the most important scene from the input image. In this paper, we present a new method to get saliency map. First, we evaluate the salience value of each region by global contrast based spatial and color feature. Second, the salience values of the first stage are used to optimize the background and foreground queries (seeds), and then manifold ranking is employed to compute two phase saliency maps. Finally, the final saliency map is got by combining the two saliency map. Experiment results on four datasets indicate the significantly improved accuracy of the proposed algorithm in comparison with eight state-of-the-art approaches.