# Saliency Detection with Bilateral Absorbing Markov Chain Guided by Depth Information
## 4 Conclusions and Future Work  
In this paper, we propose a RGB-D saliency detection model with the bilateral absorbing Markov chain guided by depth information. Using the explicit combination of depth and color information, we first generate the low-level saliency cues based on the background prior and contrast prior. Then, to overcome the existing drawbacks in the absorbing Markov chain model, we propose a serial of methods: the background seed screening mechanism (BSSM) for boundary touch cases and the cross-modal multi-graph learning model for multi-modal fusion. Moreover, considering the limitation of local intrinsic correlation, a non-local intrinsic correlation is introduced to improved two-layer sparse graph. Based on the optimized bilateral absorbing Markov chain model, we obtain the mid-level saliency maps. Finally, we design a depth-guided optimization module to get more accurate high-level saliency map. The optimization module consists of two sub-modules: the cellular automata to optimize the integrated saliency map in the color space and the suppression-enhancement function pair to refine the saliency map in the depth space. Compared with most of the algorithms mentioned in this article, our method alleviates the boundary touch problem well and greatly suppresses the background. The comprehensive comparisons and ablation study on three RGB-D saliency detection datasets have demonstrated that the proposed method is effective and robust in various scenarios both qualitatively and quantitatively.  
The literature [51] builds a new salient person (SIP) dataset with quite challenging which covers diverse real-world scenes from various viewpoints, poses, occlusion, illumination, and background. Moreover, deep learning-based RGB-D saliency detection methods [51; 54; 55] have developed vigorously and achieved the qualitative leap. Therefore, we look forward to extending our work to the deep learning in the future, exploring the complementarity of depth information and color information more fully, and dedicating ourselves to the studying of the saliency detection algorithm in real-world scenes.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Methods** & **DCMC** & **CDCP** & **DTM** & **D\({}^{3}\)Net** & **BBS-Net** & **UC-Net** & **OURS** \\ \hline
**Year** & 2016 & 2017 & 2020 & 2020 & 2020 & 2020 & 2020 \\
**Platform** & Matlab & Matlab & Matlab & PyTorch & PyTorch & PyTorch & Matlab \\
**Image size** & 640 \(\times\) 480 & 640 \(\times\) 480 & 640 \(\times\) 480 & 224 \(\times\) 224 & 352 \(\times\) 352 & 352 \(\times\) 352 & 640 \(\times\) 480 \\
**FLOPs(G)** & 3.0891 & **1.2565** & 0.4104 & 55.0722 & 31.1396 & 16.1502 & **0.2002** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Computational complexity comparison with traditional-based and deep learning-based RGB-D saliency detection methods. **Red**, green and blue indicate the best, second and third performances.  
**Author Contributions:** Conceptualization, J.W.; validation, G.H., P.L., and H.Y.; formal analysis, J.W.; investigation, J.W., G.H., P.L., and H.Y.; original draft preparation, J.W.; review and editing, J.W., G.H., P.L., H.Y., H.L., and Q.L.; funding acquisition, G.H., P.L., and H.Y. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China, grant number 61602432 and 61401425.  
**Data Availability Statement:** Data sharing not applicable.  
**Acknowledgments:** The authors would like to thank the anonymous reviewers for their constructive comments.  
**Conflicts of Interest:** The authors declare no conflict of interest.