# Salient Region Detection Using Diffusion Process with Nonlocal Connections
## 1 Introduction  
Saliency detection, which aims to find the most noteworthy region in a scene, is becoming increasingly important, especially when the amount of image is explosively increasing in the age of Big Data. It has been effectively applied in many computer vision tasks, such as image segmentation [1], object detection and recognition [2; 3], and image compression [4]. Many saliency detection methods have been proposed. These models can generally be categorized into top-down and bottom-up methods in terms of the mechanisms. Top-down methods [5; 6] are task-driven which generally require supervised learning and need to exploit high-level human perceptual knowledge. Bottom-up methods [7; 8; 9; 10; 11; 12; 13; 14; 15] are data-driven and usually exploit low-level cues, such as features, colors, and spatial distances to construct saliency maps. Most bottom-up methods adopt compactness [8; 9; 16], uniqueness [7; 13; 14; 15], and background cues [10; 11; 12]. Most compactness-based methods [8; 9; 16] consider the spatial variance of features. Generally, salient region has a low compactness variance due to the tight distribution, whereas the feature of background is distributed over the entire image, which means the higher compactness variance. However, the background regions may be wrongly highlighted, especially when the feature of background also has a compact distribution. Contrast to the compactness-based methods, the most uniqueness-based methods, consider the difference between image pixels or regions. According to the contrastive reference regions, these methods can be roughly divided into local and global contrast-based methods. Local contrast-based methods [13; 14] obtain the uniqueness of pixels or regions with respect to their neighborhoods, while global contrast-based methods [7; 15] restrict the reference regions to the entire image. However, the local contrast-based methods tend to highlight the edges of salient region rather than the whole salient region, while the global contrast-based methods are inclined to emphasize the entire image in some cases. Background-based methods [10; 11; 12] construct the saliency map via considering the location property of the background region in the image. On the contrary, center prior directly introduces the location prior knowledge of the salient object itself. But substantially, both of them are the explanations of feature distribution in the spatial dimension, and they are both motivated by the psychophysical observations that salient objects seldom touch the image boundary, whereas the background regions can be easily connected to the boundary. However, the background prior may be ineffective because the salient regions do not always appear at the center of image. Although the above-mentioned methods have achieved good performances in some respects, every low-level cue has its own limitations. To address these issues, many combined algorithms are proposed. Diffusion-based methods [9; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36], which propagate the saliency information on the graph, are one of the effective methods. [PERSON] et al. [18] constructed a conditional random field to combine multiple features for salient object detection. [PERSON] et al. [20] applied a Gaussian Mixture Model (GMM) to cluster superpixels, and calculated the saliency value using compactness metric with modified PageRank propagation. [PERSON] et al. [21] presented a data-driven approach to aggregate the saliency that generated by multiple individual saliency detection methods using a conditional random field. [PERSON] et al. [22] formulated the problem of salient object detection in images as an automatic labeling problem on the vertices of a weighted graph. [PERSON] et al. [23] ranked the similarity of the image elements (pixels or regions) with foreground cues or background cues via graph-based manifold ranking. [PERSON] et al.