# Saliency Detection with Bilateral Absorbing Markov Chain Guided by Depth Information
## 1 Introduction
[28] design a local background enclosure (LBE) feature to capture the spread of angular directions, which quantifies the proportion of the object boundary that is in front of the background from the depth map. In [33], [PERSON] et al. propose a multi-stage salient object detection framework based on minimum barrier distance transformation and multi-layer cellular automata (MCA). The framework integrates multiple visual features and priors including background prior, 3-D spatial prior and depth bias. In general, the depth-feature based method is an intuitive and simple to achieve the RGB-D saliency detection, which ignores the potential attributes in the depth map. By contrast, the depth-measurement based method aims to refine the saliency results by using implicit information. However, limited by the technology of the depth sensor, not all depth information is accurate and practicable. In another word, when the depth maps are accurate, they can provide precise depth information to facilitate saliency detection, on the contrary, they may cause detection failure when the depth maps are poor. In order to handle this problem, [PERSON] et al. [37] present a depth confidence measure to assess the reliability of the depth map and control the fusion ratio of depth features and color features in the saliency model. In addition, in [38], a novel saliency detection model is proposed that combines the implicit and explicit features of the depth map, its main idea is to transfer the existing RGB saliency detection model to RGB-D images with the help of depth constraint, so that it can inherit the saliency performance of RGB image. To a certain extent, the utilization efficiency of depth information is improved, but it also has a problem that the algorithm greatly relies on the performance of the RGB saliency detection algorithm. Therefore, how to effectively fuse depth information to enhance the detection of salient objects is still challenging. Moreover,the detection results of the above algorithms are mostly not ideal for scenes where the object touches the boundary. To tackle these problems, we propose a saliency detection model with the bilateral absorbing Markov chain guided by depth information. The model includes three progressive processing stages. At the first stage, we explicitly combine depth features with color features to calculate the low-level saliency information based on background prior and contrast prior. In the second stage, we design a bilateral absorbing Markov chain model based on the background seed selection mechanism and cross-modal multi-graph learning model. In this stage, we can obtain mid-level foreground-based and background-based saliency maps by using low-level saliency cues of first stage. In the final stage, to further improve the performance of our algorithm, we propose a depth-guided optimization module to obtain a more homogeneous salient region. The main contributions of our paper can be summarized as:  
1. A multi-stage RGB-D saliency detection framework with the bilateral absorbing Markov chain model is proposed. The framework can make full use of the explicit and implicit information in the depth map and explore the complementary relationship between the modes. 2. The background seed screening mechanism is designed to solve the boundary touch problem. Moreover, the cross-modal multi-graph learning model is designed for implicitly fusing color and depth information by the learning. 3. To preferably highlight the salient regions, we design a depth-guided optimization module which combines cellular automata and suppression-enhancement function pair.