Salient region detection via color spatial distribution determined global contrasts

In this paper, we propose a novel salient region detection method via color spatial distribution determined global contrasts. First, original image is preprocessed by a texture suppression approach, and segmented into superpixels. After that, the color spatial distribution of all superpixels is computed. Then, based on values of the distribution in whole image and boundaries of image, some superpixels are determined as foreground and background queries. Next, two global contrasts based on these queries are computed respectively to produce two different saliency maps. Ultimately, color spatial distribution and the two saliency maps are accumulated to generate final saliency map. Our approach is evaluated on M-SRA 1000 dataset, and the experimental results demonstrate superior performance of our method to eight state-of-the-art approaches.