By focusing on the problem of visual saliency detection in images

an algorithm for saliency detection based on background prior and multi-scale analysis is proposed. The method consists of four steps. First

the original image is decomposed into super pixels. The sizes of salient regions vary;thus

the super pixel scale has a significant effect on the detection results. Therefore

the image is analyzed with different super pixel scales. Second

background region is extracted. When extracting the background region

three rules are used

namely

boundary

connectivity

and feature difference among the super pixels. The super pixels of the image are classified into background and foreground. Third

according to feature differences between the super pixel and background prior

the background-based saliency of the super pixels is calculated. Similarly

the foreground-based saliency can also be computed. The saliency map can be generated by integrating background-based saliency and foreground-based saliency. Finally

saliency maps under different scales are fused to obtain the final saliency map. To verify the efficiency of theproposed algorithm

we used four datasets

namely

MASR-1000

ECSSD

SED

and SOD datasets. The results are compared with state-of-the-art algorithms. We compare our algorithm and other state-of-the-art algorithms by four indicators: precision

recall

f-measure

and mean absolute error (MAE). Experimental results show that the proposed algorithm outperformsother current popular algorithms on MSRA-1000

SED

and SOD datasets.On the ECSSD dataset

our algorithm is similar to the manifold ranking algorithm. The average values of precision

recall

F-measure

and MAE are 0.7189

0.6999

0.7086

and 0.0423

respectively. In this paper

a novel saliency detection algorithm is proposed. According to the proposed algorithm

the original image is analyzed in multi-scales.Visual saliency is computed by using the background prior. Experimental results show that the proposed algorithm can be successfully used in salient object detection and object segmentation in natural images.