# Saliency Detection with Bilateral Absorbing Markov Chain Guided by Depth Information
## 1 Introduction  
The salient object detection (SOD) is a fundamental task in computer vision, which attempts to imitate the human visual attention mechanism to locate and segment the interesting or attractive regions in a scene. It has been widely applied to a variety of vision tasks, such as image segmentation [1], resizing [2], enhancement [3], quality assessment [4], recognition [5], and matching [6]. In fact, the human visual system can not only intuitively capture the appearance of objects, but also perceive the depth information from the scene. Benefiting from the development of 3D sensing technology, the depth information can be captured more conveniently and accurately. Therefore, the RGB-D saliency detection using depth information is attracting more and more attention. Moreover, the effectiveness of depth information has been fully proved in other computer vision tasks, such as motion segmentation [7] and people re-identification [8]. Given a pair of RGB-D (RGB + depth) images, the task of the RGB-D saliency detection aims to predict a saliency map and extract the salient regions by exploring the complementary information between color image and depth data. Furthermore, existingRGB-D saliency detection models mainly use depth information in two ways. One is based on depth features [9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25], which focuses on taking depth information as an explicit supplementary feature of color features. In [12], [PERSON] et al. calculate the saliency map with additional depth information through color contrast, depth contrast, and spatial bias extended from 2D to 3D, which also proves that depth information is beneficial to visual saliency analysis in complex scenes. In order to fully explore the potential color and depth cues in the whole saliency processing process, [PERSON] et al. [16] propose an evolution strategy to introduce depth information into super-pixel generation, initial saliency map generation, and saliency propagation. In [24], [PERSON] et al. propose a united stereoscopic saliency model, which combines depth-guided background prior, boundary background, and compactness based on disparity to estimate the initial saliency map. The map is refined by using the spatial dissimilarity features under reduced dimensions and central preference. [PERSON] et al. [17; 18] directly use the depth map to generate the depth feature saliency and merge it with the color features saliency, then optimize the saliency map by combining the center dark channel prior (CDCP) or background elimination model. In [21], [PERSON] et al. generate different saliency measures based on multi-level features at different scales and perform discriminative saliency fusion through a random forest regressor to obtain the final saliency result. Aiming at the problem that the robustness of the saliency detection algorithm is not satisfied in some complex situations containing multiple objects or complex background, [PERSON] et al. [20] propose a multilayer backpropagation algorithm based on depth mining, which extracts depth cues from four different saliency layers to improve performance. The other is based on depth measurement [26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36], which aims to obtain implicit attributes such as shape and contour from the depth map by designing depth measurement algorithms. [PERSON] et al. [27] propose the normalized depth prior and the global-context surface orientation prior. These prior can highlight near objects, weaken distant objects and reduce the saliency of severely inclined surfaces (such as the ground plane or ceilings). In [26], instead of using absolute depth, [PERSON] et al. propose an anisotropic center-surround difference (ACSD) measure that considers the global depth structure to calculate and perceive the depth saliency map. Since the background usually contains the regions with a large change in depth compared to the neighborhood, this leads to a higher contrast in this region. In response to this problem, [PERSON] et al.