Discriminative saliency propagation with sink points

• A discriminative similarity metric is proposed to distinguish similar regions.
• We propose an efficient distribution guided background based weak saliency method.
• A discriminative propagation mechanism is developed to optimize coarse saliency maps.
• Several new refinements are utilized to enhance the visual quality of the output.
• We make a good balance between saliency detection accuracy and computational cost.

Salient object detection is still very challenging especially in images with complex or cluttered background. In this paper, we present an efficient and discriminative framework to address it. In specially, a discriminative similarity metric is first proposed by measuring the chi-square distance in a new constructed feature space. Then, we apply it to calculate a background based coarse saliency map by introducing distribution prior to remove foreground noises in the image boundaries. Based on manifold ranking, a robust saliency propagation mechanism is further developed to highlight salient object and simultaneously suppress background region by setting appropriate sink points. Finally, several simple refinement techniques are utilized to generate pixel-wise and smooth saliency maps. Extensive experimental results show the superior performance of the proposed method in terms of different evaluation metrics. In addition, the proposed framework can be also applied to the existing saliency propagation methods for significant performance boosting. We also believe that it is a good choice for subsequent applications based on the achieved high performance and acceptable computational overhead.