# Semantic Segmentation Based on Depth Background Blur
## 4 Experimental Results
### Ablation for Boketh
After adding the bokeh module, the result is improved by 3.7 points. \begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c c c c c c} \hline \hline
**Class** & **Vid** & **Pixel Acc** & **Mean Acc** & **Mean IOU** & **E.W IOU** \\ \hline P(\%) & 16.85 & 23.26 & 0.98 & 31.66 & 4.49 & 9.72 & 1.17 & 1.13 & 5.87 & 0.64 & 0.29 & 3.95 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The category proportion of the CamVid training dataset. \begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c c c c c c c} \hline \hline
**Method** & **Pixel Acc** & **Mean Acc** & **Mean IOU** & **E.W IOU** \\ \hline (Re)FN-8s & 80.3 & 57.04 & 25.65 & 57.23 & 48.19 & 53.33 & 70.88 & 59.84 & 67.83 & 31.44 & 49.49 & 60.06 & 59.75 & 52.87 & 57.36 & 59.74 & 36.11 & 51.46 & 48.23 & 69.54 & 65.32 & 55.6 \\ \hline (Re)FCN-8s + Fusion & 87.81 & 59.58 & 29.98 & 61.31 & 49.68 & 55.17 & 74.37 & 61.52 & 68.40 & 23.57 & 52.73 & 60.86 & 64.43 & 56.33 & 61.52 & 61.73 & 40.85 & 58.89 & 49.20 & 74.42 & 69.21 & 58.6 \\ Improved & 2.51 & 2.54 & 4.33 & 4.08 & 1.49 & 1.84 & 3.49 & 1.68 & 0.57 & 1.13 & 3.24 & 0.8 & 4.68 & 3.46 & 4.16 & 1.99 & 4.74 & 7.43 & 0.97 & 4.88 & 3.89 & 3.0 \\ \hline (Re)FCN-8s + Shrink & 88.14 & 60.38 & 31.37 & 59.44 & 52.52 & 55.58 & 76.41 & 61.98 & 70.58 & 33.70 & 52.66 & 62.31 & 63.62 & 56.01 & 61.00 & 62.70 & 41.43 & 60.11 & 50.22 & 75.00 & 63.33 & 59.2 \\ Improved & 2.84 & 3.34 & 5.72 & 2.21 & 4.33 & 2.25 & 5.53 & 2.14 & 2.75 & 2 Figure 6: Examples of the output before and after adding the bokeh module on the PASCAL VOC 2012 dataset, our resulting contour is much smoother. The first three rows are the results of experiments on FCN, and the last four rows are the results of experiments on Hyperseg. (**a**) Image; (**b**) (Re)Seg.Network; (**c**) (Re)Seg.Network+bokeh; (**d**) Ground Truth. We compared the advanced feature maps of 20 channels (excluding background channels) in the last layer of HyperSeg [21] before and after adding bokeh. The sensitivity of the network to the categories of interest is higher after adding bokeh, as shown in Figure 8. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Method** & **\(\mathbf{\text{core}}\)** & **\(\mathbf{\text{core}}\)** & **\(\mathbf{\text{core}}\)** & **\(\mathbf{\text{core}}\)** & **\(\mathbf{\text{core}}\)** & **\(\mathbf{\text{core}}\)** & **\(\mathbf{\text{core}}\)** & **\(\mathbf{\text{core}}\)** & **\(\mathbf{\text{core}}\)** & **\(\mathbf{\text{core}}\)** & **\(\mathbf{\text{core}}\)** \\ \hline (Re)BiSeNet [14] & 92.15 & 82.11 & 10.48 & 94.34 & 77.81 & 89.16 & 39.03 & 55.23 & 68.58 & 31.87 & 63.59 & 23.65 & 60.7 \\ \hline (Re)HyperSeg [21] & 94.39 & 88.66 & 36.93 & 97.87 & 90.96 & 91.70 & 74.94 & 75.68 & 90.95 & 66.98 & 86.66 & 31.66 & 77.3 \\ \hline (Re)BiSeNet + bokeh & 93.03 & 85.55 & 9.15 & 94.61 & 78.83 & 89.58 & 42.61 & 52.72 & 65.74 & 38.26 & 59.16 & 63.12 & 64.4 \\ Improved & 0.88 & 3.44 & \(-\)1.33 & 0.27 & 1.02 & 0.4 & 3.58 & \(-\)2.51 & \(-\)2.85 & 6.39 & \(-\)4.43 & 39.47 & 3.7 \\ \hline (Re)HyperSeg + bokeh & 95.53 & 92.40 & 31.28 & 97.96 & 92.07 & 92.43 & 72.85 & 78.22 & 91.65 & 70.33 & 87.15 & 68.68 & 80.9 \\ Improved & 1.14 & 3.74 & \(-\)5.6 & 0.09 & 1.11 & 0.73 & \(-\)2.09 & 2.54 & 0.70 & 3.35 & 0.49 & 37.02 & 3.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Accuracy result on the CamVid val dataset. Figure 7: Examples of the output before and after adding the bokeh module on the CamVid dataset. It is obvious that the network is sensitive enough to recognize the background (the black part in picture (**b**,**c**)) after adding the bokeh module. The first row is the results of experiments on BiSeNet and the second row on HyperSeg. (**a**) image; (**b**) (Re)Seg.Network; (**c**) (Re)Seg.Network+bokeh; (**d**) ground truth. Figure 8: Comparison of advanced feature maps and prediction: (**a**) feature map and prediction of the HyperSeg network before adding bokeh, the last row is the input image. (**b**) Feature map and prediction of the HyperSeg network after adding bokeh, the last row is the ground truth.