# Saliency Detection with Bilateral Absorbing Markov Chain Guided by Depth Information
## 3 Experiments and Discussion
### Comparisions with State-of-the-Art Methods
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Year**} & \multicolumn{6}{c}{**NLPR**} & \multicolumn{6}{c}{**NJU2K**} & \multicolumn{6}{c}{**STERE**} \\ \cline{3-14}  & & \(S_{\pm}\uparrow\) & \(E_{m}\uparrow\) & \(F_{\beta}\uparrow\) & **MAE\(\downarrow\)** & \(S_{\pm}\uparrow\) & \(E_{m}\uparrow\) & \(F_{\beta}\uparrow\) & **MAE\(\downarrow\)** & \(S_{\pm}\uparrow\) & \(E_{m}\uparrow\) & \(F_{\beta}\uparrow\) & **MAE\(\downarrow\)** \\ \hline
**ACSD** & 2014 & 0.6728 & 0.7418 & 0.5345 & 0.1787 & 0.6992 & 0.7863 & 0.6964 & 0.2021 & 0.6919 & 0.7932 & 0.6607 & 0.2000 \\
**DESM** & 2014 & 0.5722 & 0.6978 & 0.5633 & 0.3124 & 0.6648 & 0.6824 & 0.6321 & 0.2835 & 0.6425 & 0.6751 & 0.5942 & 0.2951 \\
**LHM** & 2014 & 0.6298 & 0.8131 & 0.6636 & 0.1077 & 0.5136 & 0.7082 & 0.6383 & 0.2048 & 0.5617 & 0.7700 & 0.7029 & 0.1719 \\
**GP** & 2015 & 0.6545 & 0.8045 & 0.6593 & 0.1461 & 0.5265 & 0.7161 & 0.6554 & 0.2106 & 0.5876 & 0.7842 & 0.7106 & 0.1822 \\
**DCMC** & 2016 & 0.7244 & 0.7856 & 0.6141 & 0.1167 & 0.6861 & 0.7905 & 0.7173 & 0.7176 & 0.7306 & 0.8314 & 0.7425 & 0.1476 \\
**LBE** & 2016 & 0.7619 & 0.8550 & 0.7355 & 0.0813 & 0.6952 & 0.7913 & 0.7400 & 0.1528 & 0.6601 & 0.7485 & 0.5951 & 0.2498 \\
**SE** & 2016 & 0.7561 & 0.8388 & 0.6915 & 0.0913 & 0.6642 & 0.7722 & 0.7335 & 0.1687 & 0.7082 & 0.8250 & 0.7476 & 0.1427 \\
**CDCP** & 2017 & 0.7270 & 0.8001 & 0.6076 & 0.1121 & 0.6685 & 0.7472 & 0.6238 & 0.1803 & 0.7134 & 0.7964 & 0.6655 & 0.1489 \\
**CDB** & 2018 & 0.6286 & 0.8094 & 0.6132 & 0.1142 & 0.6239 & 0.7448 & 0.6484 & 0.2028 & 0.6151 & 0.8079 & 0.7127 & 0.1655 \\
**DTM** & 2020 & 0.6787 & 0.7656 & 0.5271 & 0.1611 & 0.6490 & 0.7454 & 0.6082 & 0.2217 & 0.7049 & 0.7978 & 0.6585 & 0.1910 \\
**OURS** & 2020 & **0.8131** & **0.8751** & **0.7845** & **0.0712** & **0.7361** & **0.7925** & **0.7494** & **0.1359** & **0.7774** & **0.8347** & **0.7724** & **0.1110** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Quantitative comparisons of different RGB-D saliency detection methods on three popular datasets. **Red**, green and blue indicate the best, second and third performances. \(\uparrow\) denotes larger is better, and \(\downarrow\) denotes smaller is better. Figure 10: PR curves of the proposed and 10 state-of-the-art methods on 3 datasets. (**a**) NLPR dataset; (**b**) NJU2K dataset; (**c**) STERE dataset. Figure 11: _Cont._  
Here combined with examples to vividly expand the above four aspects. First, as shown in the 7-th and 8-th rows of Figure 10(a), the 3-th, 5-th, and 7-th rows of Figure 10(b), and the 8-th row of Figure 10(c), only the GP algorithm has certain resistance to boundary touch problem, but when the background is complex and the depth map is poor, as shown in the 3-th rows of Figure 10(b), the detection will fail. In contrast, our algorithm achieves better results in various scenes when encountering this situation. Then, from the 3-th, 4-th, and 6-th rows of Figure 10(a), we can find that most of the algorithms cannot effectively remove the background in front of the salient objects due to the interference from the depth near the camera. However, our method can availably eliminate them by using learning fusion. Moreover, as shown in the 7-th row of Figure 10(b), the 8-th and 10-th rows of Figure 10(c), our method works well when the color appearance of salient object is similar to the background. Finally, our model is still robust under the condition of poor depth map quality, which is demonstrated in the 3-th and 4-th rows of Figure 10(b), the 1-th, 2-th, 3-th, and 6-th rows of Figure 10(c). Figure 11: Visual examples of different methods on different datasets. (**a**) NLPR dataset; (**b**) NJU2K dataset; (**c**) STERE dataset. In general, our algorithm has better robustness in the various complex scenarios. Especially, when the salient objects touch the image boundary or the depth map quality in the dataset is uneven, our method still has a good performance, which can obtain the uniform and highlighted salient objects.