Saliency detection is a fundamental part of computer vision applications

the goal is to detect important pixels or regions in an image which attracts human visual attention most. Recently

people have proposed boundary prior

or background information to enhance saliency detection. Such methods even achieve state-of-the-art result

suggesting that boundary prior is effective. Compared with most existing bottom-up methods which consider saliency based on the contrast between salient objects and their surrounding regions

boundary prior characterizes the spatial layout of image regions with respect to image boundaries. Inspired by this idea

we propose image composition prior to detect saliency. Observing from images

we find salient objects usually placed in center regions while background lies in boundary regions. And images are usually formed with some composition rules

such as Rule of Thirds. We propose composition prior method by assuming objects are distributed near composition lines. We select regions near composition lines as initial seeds

and compute saliency according to feature relevance. To be specific

firstly

we segment the image into multi scales and construct a close-loop graph where each node is a super pixel. Secondly

we use nodes which near composition lines as queries

and extract their features to rank the relevancies of all the other regions by Manifold Ranking

and then compute saliency based on the ranking result. Thirdly

according to the last step

we iteratively refine saliency in the perspective of both object and background. Then assign the saliency value to each pixel. Considering the distinctness of different pixels in the same region

we need to correct their saliency. We choose to add a correction value to each pixel based on their distance to feature center. Finally

the saliency detection is carried out by integrating multi-scale saliency. In comparison experiments on datasets of MSRA-1000

CSSD

and ECSSD

our method performs well when against the state-of-the-art methods. It gets highest precision on three datasets (92.6%

89.2%

and 76.6% respectively). The average run time of a single image is 0.692

which still has some advantages compared with other algorithms. This study presents a new salient detection method based on composition prior. Human vision has the tendency of detecting saliency from regions near composition lines rather than image boundaries. Composition prior detects saliency based on human vision mechanism. Experimental results demonstrate detect saliency in the perspective of image composition is reasonable

and using composition prior can improve the detecting accuracy.