Saliency detection aims to automatically identify and localize the important or attractive regions from an image.In the recent years

many researchers have given particular attention to saliency detection and took it as an important step in image processing.Saliency detection has been applied to many computer vision tasks and applications

such as image retrieval

object detection and recognition.Although saliency detection has been studied for many years

there are still certain shortcomings.For example

the detection in complex scenes is inaccurate or the results of the detection contain background noises.Considering that several existing methods of image saliency detection cannot suppress the background regions effectively

or cannot highlight the complete object regions clearly

a novel saliency detection method combining background priori and foreground priori information was proposed to further improve accuracy.The background prior is an assumption that the regions along the image boundaries are background regions

and the foreground prior is to calculate a convex hull to locate the foreground object regions. The region saliency of an image is defined as its similarity to the foreground in addition to being defined as its contrast to the background.Therefore

background and foreground can be extracted with prior

and all the regions of an image can be compared with these background and foreground to generate a saliency map.First

we selected the superpixels from image boundaries as the background regions to compute a background-based saliency map based on the dissimilarity between each region and the background regions.Second

we applied the convex hull from interest points to approximately locate the foreground object.Convex hull of original image and filtered image were calculated because there were not only salient regions inside the convex hull

but also the background regions

and the intersection regions of the two convex hull regions were obtained to remove background regions to some extent.Then

the intersection regions were combined with the background-based saliency map to select the foreground object regions

so foreground-based saliency map could be generated based on the similarity between each region and the foreground object regions.Finally

we integrated the two saliency maps utilize their respective advantages because the background-based saliency map could highlighted the object more uniformly and the foreground-based saliency map could better suppress the background noises.Then

the unified saliency maps was further refined to obtain a smoother and accurate saliency map. To test the performance of the proposed algorithm

experiments were conducted on the MSRA10K datasets

which contained 10 000 images and was one of the largest publicly available datasets.The results demonstrated that the saliency map of the proposed algorithm are closer to the ground truth and the proposed method performed favorably against the state-of-the-art methods in terms of accuracy and efficiency.The average precision

average recall

F-measure

MAE

and average running time of the proposed method are 87.9%

79.17%

0.852 6

0.113

and 0.723 s

respectively. Saliency detection is a promising preprocessing operation in the field of image processing and analysis.We proposed a new method to detect saliency based on a combination of two kinds of prior information.The detection results of the proposed algorithm could not only effectively suppress the background noises

but also clearly highlight the object regions

thus improving the accuracy of the detection.