Visual saliency

which is a relative property that depends on the degree of difference between a pixel or a region and its background

is difficult to obtain accurately. IG approach

a global-based contrast method

achieves good results on a public dataset. However

this method ignores important local details and inhibits contrast definition between target and background. Thus

this method needs to be improved in terms of the local details. This study proposes a novel saliency detection method based on global consistency and local difference

in which global contrast and local difference are considered two factors for saliency detection. In addition

the application of saliency characteristics to target segmentation is proposed and has achieved better experimental results. Different angles show different saliency characteristics. Thus

we compute image saliency from two angles: global feature and local details. The method consists of three basic steps. First

we detect the global consistency by IG

and the global saliency value of the target is achieved by the detection. However

to avoid ignoring the local details and inhibiting the contrast definition between the target and background

we propose the use of the NIF algorithm to overcome the drawbacks of IG. Second

we introduce the NIF method to detect the local difference of the target. Concretely

each pixel is the center pixel

and the square neighborhood of each center pixel is taken as its local scope. Thus

for every pixel

we can obtain corresponding local difference values in relation to its local scope. Finally

we derive the final saliency value by combining the saliency values that originate from the global and local contrasts. The saliency map produced by the proposed algorithm achieved a consistent effect compared with artificial segmentation map. To verify the efficiency of the proposed method

experiments are performed using MSRA-1 000 dataset

which is one of the largest public available datasets. Results show that our method outperforms several existing salient object detection methods in terms of precision

recall and F-feasure. In comparison with other popular algorithms

our algorithm has a higher efficiency in image segmentation. In this paper

we propose a novel saliency detection method

which treats an image as being composed of global consistency and local difference. We mainly focus on the problem of the local difference of the target and combine both global contrast and local difference for final saliency detection. Compared with some popular methods on a public dataset

our approach achieves the best results in terms of saliency measure evaluation. Furthermore

it obtains good performance in integrating saliency feature in image segmentation. Experimental results show that the algorithm can detect natural images more accurately and can be successfully applied to natural image segmentation.