# An Improved Image Semantic Segmentation Method Based on Superpixels and Conditional Random Fields
## 4 Experimental Evaluation
### Experimental Results
#### 4.3.2 Quantitative Analysis  
* PASCAL VOC 2012 Dataset  
The per-class IoU scores on VOC dataset among FCN-8s, the proposed method, and other popular methods are shown in Table 2. It can be observed that our method achieves the best performance in  
Figure 11: Visual results on the validation set of Cityscapes. From left to right: the original image, SLIC segmentation, FCN-8s results, DeepLab-v2 results, our results, and the ground truth. most categories, which is consistent with the conclusion obtained in qualitative analysis. In addition, the proposed method outperforms prior methods in mIoU metric; it reaches the highest 74.5% accuracy. This work proposed a post-processing method to improve the FCN-8s result. During the implementation procedure, the superpixel and the CRF are used subsequently to improve the coarse results extracted by FCN-8s. Therefore, the comparison with FCN-8s is the most primary and direct way to illustrate the improvement level of our method. Meanwhile, to further evaluate the performance of our method, the comparison with DeepLab has been made. The IoU, mIoU, and PA scores of FCN-8s and DeepLab-v2, and our methods are given in Figure 12. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **FCN-8s** & **Zoom-Out [38]** & **DeepLab-v2** & **CRF-RNN [20]** & **GCRF [39]** & **DPN [40]** & **Our Method** \\ \hline areo & 74.3 & 85.6 & 86.6 & 87.5 & 85.2 & **87.7** & 85.5 \\ bike & 36.8 & 37.3 & 37.2 & 39.0 & 43.9 & **59.4** & 40.1 \\ bird & 77.0 & 83.2 & 82.1 & 79.7 & 83.3 & 78.4 & 83.1 \\ boat & 52.4 & 62.5 & 65.6 & 64.2 & 65.2 & 64.9 & **66.3** \\ bottle & 67.7 & 66.0 & 71.2 & 68.3 & 68.3 & 70.3 & **74.2** \\ bus & 75.4 & 85.1 & 88.3 & 87.6 & 89.0 & 89.3 & **91.3** \\ car & 71.4 & 80.7 & 82.8 & 80.8 & 82.7 & **83.5** & 82.3 \\ cat & 76.3 & 84.9 & 85.6 & 84.4 & 85.3 & 86.1 & **87.5** \\ chair & 23.9 & 27.2 & **36.6** & 30.4 & 31.1 & 31.7 & 33.6 \\ cow & 69.7 & 73.2 & 77.3 & 78.2 & 79.5 & 79.9 & **82.2** \\ table & 44.5 & 57.5 & 51.8 & 60.4 & **63.3** & 62.6 & 62.3 \\ dog & 69.2 & 78.1 & 80.2 & 80.5 & 80.5 & 81.9 & **85.9** \\ horse & 61.8 & 79.2 & 77.1 & 77.8 & 79.3 & 80.0 & **83.0** \\ mbike & 75.7 & 81.1 & 75.7 & 83.1 & **85.5** & 83.5 & 83.4 \\ person & 75.7 & 77.1 & 82.0 & 80.6 & 81.0 & 82.3 & **86.6** \\ plant & 44.3 & 53.6 & 52.0 & 59.5 & **60.5** & **60.5** & 56.9 \\ sheep & 68.2 & 74.0 & 78.2 & 82.8 & 85.5 & 83.2 & **86.3** \\ sofa & 34.1 & 49.2 & 44.9 & 47.8 & 52.0 & **53.4** & 49.4 \\ train & 75.5 & 71.7 & 79.7 & 78.3 & 77.3 & 77.9 & **80.4** \\ tv & 52.7 & 63.3 & 66.7 & 67.1 & 65.1 & 65.0 & **69.9** \\ mIoU & 62.7 & 69.6 & 71.2 & 72.0 & 73.2 & 74.1 & **74.5** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Per-class IoU score on VOC dataset. Best performance of each category is highlighted in bold. Figure 12: IoU, mIoU, and PA scores on the PASCAL VOC 2012 dataset. It can be observed from Figure 12, compared with FCN-8s, that the proposed method significantly improves the IoU score in every category with higher mIoU and PA scores. In addition, our method is better than DeepLab-v2 in most categories (except for aero plane, car and chair). The detailed improvement statistics are shown in Figure 13.  
As shown in Figure 13, our method can be significantly improved compared with FCN-8s, which reach up to 11.8% in mIoU and 17.4% in PA, respectively. For DeepLab-v2, the improvements of mIoU and PA are 3.3% and 1.7%. Moreover, Figure 13 intuitively shows the improvement level using our method in comparison with FCN-8s and DeepLab. The improvement can be clearly observed in 100% (21/21) of categories when compared with FCN-8s. Meanwhile, there is 85.71% (18/21) of categories improvement in comparison withDeepLab-v2. In summary, our method achieved the best performance among these three methods. \(\bullet\)Cityscapes Dataset  
We conducted an experiment on the Cityscapes dataset, which differs from the previous one in the high-resolution images and the number of classes.