Salient object detection via local saliency estimation and global homogeneity refinement

• We use local saliency and global homogeneity to detect saliency.
• We model it as a competition of the strengths of foreground and background seeds.
• Our approach achieves remarkable precision–recall performance.
• The performance is not sensitive to the population of the selected seeds.
• Our approach outperforms many existing methods in quantitative way.

This paper presents a new hybrid approach for detecting salient objects in an image. It consists of two processes: local saliency estimation and global-homogeneity refinement. We model the salient object detection problem as a region growing and competition process by propagating the influence of foreground and background seed-patches. First, the initial local saliency of each image patch is measured by fusing local contrasts with spatial priors, thereby the seed-patches of foreground and background are constructed. Later, the global-homogeneous information is utilized to refine the saliency results by evaluating the ratio of the foreground and background likelihoods propagated from the seed-patches. Despite the idea is simple, our method can effectively achieve consistent performance for detecting object saliency. The experimental results demonstrate that our proposed method can accomplish remarkable precision and recall rates with good computational efficiency.