# Semantic Segmentation Based on Depth Background Blur
## 4 Experimental Results
### Ablation for Boketh  
Applying bokeh to multiple segmentation networks on two datasets, comparative experiments were made. The experimental results of three FCN network architectures and HyperSeg on the PASCAL VOC dataset are shown in Table 4. As can be seen, the mean IOU of the four mentioned network architectures ((Re)FCN-32s, (Re)FCN-16s, (Re)FCN-8s, and (Re)HyperSeg) with bokeh are improved by 4.7, 4.6, 4.8, and 5.2 points, respectively. At the same time, the specific precision of FCN-8s before and after adding the bokeh module on the PASCAL VOC 2012 Val dataset is given, as illustrated in Table 5. Note that the segmentation accuracy of an individual category is significantly improved, except for 5 out of 84 comparison items. Considering the results in Table 2 and Table 5 together, we find relatively small categories, such as bicycle (0.29%), boat (0.58%) and potted plant (0.64%), make a significant contribution to accuracy improvement. Note that the segmentation accuracy of exceptional categories, such as \"Cow\" and \"Dinning Table\", decrease inversely. This is because while blurring the background, it depresses the context information to some extent. We considered it from two aspects. One is to employ the fusion of \"(Re)FCN-8s\" and \"(Re)FCN-8s + bokeh\", named \"(Re)FCN-8s + Fusion\". Another is to confine the scope of the background blur field to ensure rich context information is preserved, named \"(Re)FCN-8s + Shrink.\" The experimental results demonstrate, as shown in Table 5, that these two methods can avoid the accuracy decrease in specific categories. However, the improvement of the overall segmentation accuracy is not as good as before. As a result, the accuracy decrease in some individual categories is permitted. Qualitative examples on this dataset are shown in Figure 6. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Method** & **Pixel Acc** & **Mean Acc** & **Mean IOU** & **F.W IOU** \\ \hline (Re)FCN-32s [9] & 80.7 & 74.7 & 52.9 & 78.2 \\ (Re)FCN-16s [9] & 81.6 & 75.4 & 54.6 & 79.5 \\ (Re)FCN-8s [9] & 82.3 & 76.5 & 55.6 & 80.5 \\ (Re)HyperSeg [21] & 93.8 & 83.4 & 72.8 & 89.1 \\ (Re)FCN-32s + bokeh & 83.2 & 77.1 & 57.6 & 82.2 \\ (Re)FCN-16s + bokeh & 83.9 & 78.0 & 59.2 & 83.1 \\ (Re)FCN-8s + bokeh & 84.6 & 78.0 & 60.4 & 83.8 \\ (Re)HyperSeg + bokeh & 95.6 & 86.2 & 78.0 & 92.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The segmentation performance of three variants of FCN on the PASCAL VOC 2012 validation set, \"(Re) FCN-XXs + bokeh\" means the bokeh module is added to \"(Re)FCN-XXs\". The bokeh module improves the segmentation results on the CamVid val dataset by 3.7 in Mean IOU, as shown in Table 6. This demonstrates that the proposed bokeh module is easily embedded into a real-time network architecture. In view of the consequences of PASCAL VOC 2012, bokeh plays a vital role in the class with a small proportion of the dataset. As for CamVid, the background occupies a relatively small proportion, but the accuracy increase is clear. The proportion of the CamVid training dataset by category is presented in Table 7.  
It is clear from Table 8 that the improvement in the Unlabelled category is the most significant. Analyzing the characteristics of CamVid, it can be observed that low background proportion of the dataset itself and diversity of categories in each image leads to this. After adding the bokeh module, the image changes are not noticeable compared to the ones before the fuzzification. However, this slight improvement occurs in almost all of the Unlabelled category. Qualitative examples on this dataset are given in Figure 7. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Method** & **Pixel Acc** & **Mean Acc** & **Mean IOU** & **E.W IOU** \\ \hline (Re)BiSeNet [14] & 80.3 & 74.0 & 60.7 & 84.5 \\ (Re)HyperSeg [21] & 94.5 & 85.8 & 77.3 & 89.9 \\ (Re)BiSeNet + bokeh & 81.8 & 76.3 & 64.4 & 85.9 \\ (Re)HyperSeg + bokeh & 96.0 & 87.8 & 80.9 & 92.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Accuracy result on the CamVid val dataset.