Two-stage salient region detection by exploiting multiple priors

Saliency detection has become a valuable tool for many image processing tasks, like image retargeting, object recognition, and adaptive compression. With the rapid development of the saliency detection methods, people have approved the hypothesis that “the appearance contrast between the salient object and the background is high”, and build their saliency methods on some priors that explain this hypothesis. However, these methods are not satisfactory enough. We propose a two-stage salient region detection method. The input image is first segmented into superpixels. In the first stage, two measures which measure the isolation and distribution of each superpixel are proposed, we consider that both of these two measures are important for finding the salient regions, thus the image-feature-based saliency map is obtained by combining the two measures. Then, in the second stage, we incorporate into the image-feature-based saliency map a location prior map to emphasize the foci of attention. In this algorithm, six priors that explain what is the salient region are exploited. The proposed method is compared with the state-of-the-art saliency detection methods using one of the largest publicly available standard databases, the experimental result indicates that the proposed method has better performance. We also demonstrate how the saliency map of the proposed method can be used to create high quality of initial segmentation masks for subsequent image processing, like Grabcut based salient object segmentation.