Salient region detection based on prior has captured the attention of researchers in recent years. According to experience

the researchers assume that some regions are foreground or background and then detect salient objects based on these assumptions. However

the method based on single prior has a few weaknesses. For example

several classic methods based on center-bias prior always assume that objects are near the image center and disregard the objects that are far from the center. Methods based on background-bias prior take boundaries as background and calculate the saliency values of other regions according to their contrast with the background. Objects on boundaries cannot be highlighted. To solve these problems

a novel salient object detection algorithm based on the integration of center-bias prior and background-bias prior is proposed. First

the input image is divided into superpixels. Second

a graph is constructed with superpixels as nodes. Boundary nodes are selected as absorbing nodes

and the others are taken as transient nodes. The absorbed time from each transient node to boundary absorbing nodes is computed based on the absorbing Markov chain and taken as background-bias prior values. A single-scale background-bias prior map is obtained. Third

corner points of the input image are detected with a novel Harris corner detection algorithm. A two-dimensional Gaussian function with peak at the cluster center of corner points is constructed. The center-bias prior value of each superpixel is obtained through the Gaussian function. Fourth

the two prior maps are combined into the final saliency map. Results from multi-scale saliency maps are integrated to further improve the detection performance by different-scale superpixel segmentations. Extensive experiments of state-of-the-art methods and the method based on the integration of center-bias prior and background-bias prior are conducted on ASD

SED1

SED2

and SOD benchmark datasets. Experimental results show that the proposed method performs favorably against 10 state-of-the-art methods in terms of precision

recall

and F-measure. Compared with the method based on absorbing Markov chain

the evaluation metrics of the proposed method increased by at least 3%. Every method is also evaluated through the subjective approach. The result of the proposed method is the most excellent in detail and global aspects. In contrast to the method based on a single prior

the proposed method based on the integration of center-bias prior and background-bias prior can extract more minute features of the input image. The saliency maps not only show global contrast but also have highly detailed information. The proposed method can highlight the salient object more effectively. Single object in images can be detected accurately because of the limitation of the center-bias prior. With regard to images with more than one object

the proposed method has some imperfections.