Saliency Detection via Boundary Prior and Center Prior

Recently, as the important field of computer vision, image processing has received considerable attention. In addition, massive efforts have been made to find a favourably way to express the content of the image by extracting useful information from image. In this context, the research of salient detection comes into being, the model of saliency is used to find the highlight object in image, and then, it is convenient for the subsequent operations in image processing. Saliency detection can be applied to many modern computer vision tasks, for instance, image classification [1], image compression [2], object location [3], image segmentation [4]. In the matter of information processing methods, saliency detection algorithms can be divided into topdown and bottom-up methods. The detection algorithm of topdown approaches [5-8] always related to a particular task or target. Before the top-down algorithms find the highlight objects, these algorithms need to obtain the basic properties of target. In this case, the top-down algorithms can quickly and effectively find the salient target in image. But, these kinds of algorithms need to take the supervised learning. On the contrary, the approaches of bottom-up [4,9-12] adopt the low-level visual information without the cues of a certain target. Compared with top-down algorithms, the bottom-up approaches would be more applicable. One of the mostly used bottom-up methods, which measure the distinction between a pixel and region with its neighbourhoods [13]. Owing to lack of the prior knowledge of the object size, the approach of centre-boundary contrast often calculates the saliency in the multi-scale space; it may increase the computation complexity in some extent. In addition, some researcher adopts the boundary priority [12], they consider the boundary of image would be more likely to become the background. It is undeniable that the boundary has the high probability being the background, but not all the region in the boundary would be background. Once the salient object locates at the boundary of the image, it may lead to a poor result.