Saliency Detected Model Based on Selective Edges Prior

In the field of saliency detection, background prior has become a novel viewpoint, but how to identify the real background is challenging. In this paper, a background-identified method is proposed based on homology continuity using the extracted background features, and the identified background is applied to the following computation, improving the eventual saliency map in accuracy as well as correctness. First, the primary saliency of each superpixel produced by Mean Shift (MS) segmentation algorithm is calculated. Second, 4 edges are extracted to generate their RGB histograms, and the Euclidean distance between each two of the histograms is calculated, if the distance is smaller than a given value, these two edges are defined to be continual and more likely to be the real background. Finally, the pixels saliency is calculated using the prior background knowledge to figure the final saliency map. The results show that the proposed method outperforms other algorithms in accuracy and efficiency.