Saliency detection by aggregating complementary background template with foreground information
Hanling Zhang, Chenxing Xia, Jianhua Cui · 2018
This paper proposes an unsupervised bottom-up saliency detection approach by exploiting novel background template and foreground information. First, a discriminative feature vector is extracted from each super-pixel to cover regional color, contrast and texture information. Then we apply it to get a background based saliency map based on a background template. In order to get more accurate saliency map, we select highly confident compact foreground seeds to compute a foreground based saliency map. After fusing the two saliency maps, the integrated map is refined to achieve the final result. Experimental results show that the proposed algorithm generates high-quality saliency maps against the state-off-the-art saliency detection methods on four publicly available datasets.