# Weakly Supervised Conditional Random Fields Model for Semantic Segmentation with Image Patches
## 4 Experiments and Discussion
### Parameter Settings and Evaluation  
To show more intuitively the effect of initial superpixel K on superpixel segmentation, the three most widely used evaluation indicators are adopted. Boundary recall (BR) measures is the proportion of the target boundaries recovered by the superpixel boundaries. Achievable segmentation accuracy (ASA) is a performance upperbound measure. Under-segmentation error (UE) is an error generated by the algorithm when the image is segmented compared with ground truth. The definition of evaluation index used in [42] is adopted here. We compared the number of initial superpixels manually set on MSRC datasets, which visualizes the effect of the number of initial superpixel \(K\) on the result of superpixel segmentation. The effect of the initial number of superpixels \(K\) is shown in Figure 5.  
As shown in Figure 5, from the three evaluation indicators, the performance of image segmentation also improves with the increase of the initial number of superpixels \(K\) in a specific range. After that, it will be saturated later. In this state, although the value of \(K\) continues to increase, the segmentation performance remains basically unchanged. If the value of \(K\) is too large for the image segmentation, it will increase redundant information and the complexity of subsequent calculation.  
The accuracy of superpixel segmentation directly affects the results of subsequent ISS, and the value of \(K\) determines the size and number of superpixels. If the value of \(K\) is too small to achieve good segmentation results, while too large \(K\) will bring redundant information.  
Figure 4: A set of images and their corresponding ground-truth annotations.  
Figure 5: The effect of the initial number of superpixels \(K\) on image accuracy.  
Therefore, the appropriate \(K\) helps to achieve good segmentation results. For visual assessment, we conduct a comparison experiment on the number of superpixels \(K\) in SLIC superpixel segmentation. As shown in Figure 6 and Table 1, the proposed algorithm is feasible in generating satisfactory results on different cases.  
Figure 6b,e show the results of image segmentation from our algorithm with the parameter \(K\) being set as 310 and 332, respectively. To better compare the effectiveness of the algorithm, the experiment compares the \(K\) obtained by our proposed algorithm with the segmentation result of \(K\) around 100 (\(K\) is set manually with traditional SLIC). Figure 6a is the result of \(K\) = 250, the head and tail of the plane are not well segmented. Figure 6d shows the Figure 6 the segmentation result of \(K\) =250. The outline of the plane and some details are not very good. Figure 6b,e have better segmentation effects.  
In Table 1, although the number of superpixels is increasing, the F\({}_{1}\)-score has not increased significantly. The proposed method can determine the number of superpixels according to different images without multiple attempts to determine appropriate superpixels, which thus saves the running time cost and improves the efficiency.