In this paper, we propose a novel saliency detection algorithm. The saliency of an image element is defined not only as its contrast to the background but as its similarity to the foreground. First, we extract background seeds as well as their spatial layout information from image boundaries to compute the background-based saliency map. Second, we generate a compact foreground region from the first-stage saliency map to describe the appearance and location of the salient object and calculate the foreground-based saliency map accordingly. We integrate these two saliency maps and further refine the unified one to obtain a more smooth and accurate saliency map. Each component of the presented algorithm is evaluated on the public available datasets and the experimental results also show that the presented algorithm achieves favorable performance compared to the state-of-the-art methods.
['Zhengbing Wang', 'Guili Xu', 'Zhengsheng Wang', 'Chunxing Zhu']
Saliency detection integrating both background and foreground information