Exploiting Randomized Prim’s Algorithm and Background Contrast forSaliency Detection

The geodesic saliency method in the literature was based on the boundary and connectivity priority, which as- sumed that most of the background regions can touch the image boundaries. It cannot deal with the images with complex backgrounds or variant textures. To address such problem, we propose an improved saliency detection method by involv- ing the important foreground priority. First, the statistical results of randomized Prim's algorithm are used to generate a coarse conspicuity map, which aims to roughly estimate the potential foreground. Then, the image is over-segmented into some individual superpixels and an affinity propagation clustering method is used to group the superpixels having a simi- lar color appearance together. This is followed by the foreground probability map computation through the spatial interac- tion information between the coarse conspicuity map and superpixel based color clusters. The final saliency map is gener- ated by integrating the above foreground probability map and background color contrast in a unified way. The quantitative and qualitative comparisons on the benchmark dataset MSRA-1000 and SED show that our method outperforms many re- cent proposed state-of-the-art approaches significantly.