# Weakly Supervised Conditional Random Fields Model for Semantic Segmentation with Image Patches
## 4 Experiments and Discussion
### Comparing with the State-of-the-Art  
Here, we perform a group of experiment to evaluate the performance of weakly supervised ISS method. For reference, we compared the state-of-the-art methods, such as PLSA [43], WSDC [30], Textonboost [39], MIM [29]. Table 2 shows the segmentation performance on MSRC dataset. The proposed algorithm is compared with CCNN [44], EM-Adapt [37], MIL-ILP-seg [45], SN-B [36] and H&M [46]. The experiments of  
\begin{table}
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**Test Image and Size** & **Manually Set \(K\)** & **F\({}_{1}\)** & **Determined \(K^{\prime}\)** & **F\({}_{1}\)** \\ \hline Image 1 (320 \(\times\) 213) & 150 & 0.829 & & 275 & 0.832 \\  & 350 & 0.816 & & & \\ Image 2 (320 \(\times\)  
these methods are carried out on PASCAL VOC 2012 dataset, and the performance of image segmentation is shown in Table 3. The performance is measured in terms of pixel intersection-over-union (IoU) and mean intersection-over-union (mIoU) across 21 classes. In Tables 2 and 3, each column represents the accuracy of each semantic class, and the last column is the average accuracy of all classes. For the values in the table, the values in bold represent the best segmentation performance of this category.  
As seen in Table 2, our method provides competitive results when compared with the state-of-the-art methods on the MSRC dataset. Although the accuracy is not as high as others in some categories, yet the overall mIoU is the best among the group. Actually, our approach has produced the best results in six categories, whilst the second and the third overall best approaches, MIM and Textonboost, are dominant in eight and six categories, respectively. This shows our approach can balance in between different classes for good overall performance.  
For the results on the PASCAL VOC 2012 dataset in Table 3, our results are the second best in terms of mIoU, which is quite comparable to the best one produced by SN-B, a deep learning-based model. However, our approach significantly outperforms another deep learning model, CCNN, and two other approaches, MIL-ILP-seg and EM-Adapt. Actually, SN-B produces the best results in nine categories of objects, whilst our approach generates the best in seven categories, although the overall mIoU is only 1% lower. Again, the proposed approach seems to be more robust over different categories.  
To show the performance of our proposed algorithm more intuitively, extensive experiments were performed on the PASCAL VOC 2012 dataset. As shown in Figure 7, the experimental results are compared with the ground truth. It can be seen from the comparison of segmentation results in Figure 7. that better segmentation results can be achieved when the image object contains only one dominant (merged) superpixel or if the background is relatively simple. On the contrary, when the background of the image is more complex, the accuracy of ISS will also be reduced. In addition, for example, there are many objects in the image, so that occlusion or small shadows between these objects will affect the result of ISS.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}