Salient object detection aims to identify spatial locations and scales of the most attention-grabbing objects in a given image

which is shown to be helpful in various computer vision tasks

such as object recognition

adaptive image display

and object detection. Different from eye-fixation saliency prediction

salient object detection emphasizes the saliency and wholeness of detected objects. Thus

dealing with cluttered background and diversity of object parts within an image has always been one of the major challenges in salient object detection. Bottom-up visual saliency is commonly characterized by the contrast of primitive image features at the pixel or super-pixel levels because contrast is the most predominant factor in human cognition. In the literature

the local or global contrast is usually adopted to straightforwardly derive the saliency map

where the contrast in a certain region is calculated by comparing its feature with that of the reference regions. However

such methods using local or global contrast reference regions may fail to detect whole salient objects

especially when dealing with complicated images. We attribute their failure to unreasonable setting of contrast reference regions. To enhance the wholeness of the detected salient objects

an explicit background-driven method is proposed

in which background prior is comprehensively utilized in saliency estimation and optimization. To obtain the background regions of an image for contrast estimation

deep convolutional neural networks were initially used to learn a background map representing the likelihood of each region belonging to the background. From the obtained background map

the background regions could be segmented with a simple thresholding strategy. The learned background regions were then used as references for region contrast computation. To enhance the consistency between the foreground and background regions

enhanced graph-based optimization was adopted to propagate saliencies along the graph. Besides the conventional local connections in a k-regular graph

prior connections with virtual nodes and non-local connections between nodes belonging to background regions were also added to the graph to embed the learned background prior. To verify the effectiveness of the proposed salient object detection method

comprehensive experiments were conducted on four public saliency detection datasets

namely

ASD

SED

SOD

and THUS-10000. The results were compared with those of nine state-of-the-art methods. Four indicators (i.e.

precision

recall

F-measure

and MAE) were adopted for comparison. The average scores in precision

recall

F-measure

and MAE of our method were 0.873 6

0.795 2

0.844 1

and 0.112 2

respectively

which showed that our method outperformed other popular methods. The best results on all of the datasets were achieved using the proposed method

thereby demonstrating its effectiveness and superiority. Restricting the contrast reference regions to the background could significantly improve contrast-based saliency estimation. The background regions in an image could be effectively learned by convolutional neural networks. An enhanced graph-based optimization could fuse the saliency confidences of different parts from the same object to discover the whole salient object; thus

a more consistent and background-suppressed saliency map could be generated. Experimental results showed that the proposed method can be successfully used in salient object detection and object segmentation in natural images.