# Infrared Image Superpixel Segmentation Based on Seed Strategy of Contour Encoding
## 4 Experiment
### Results
Images in each column represent the results of: (**a**) SLIC; (**b**) SNIC; (**c**) LRW; (**d**) USEAQ; (**e**) CAS; and (**f**) our method. The expected number of superpixels was set as 150. In addition to the public data set containing natural scenes, our method was also tested on the medical CT imaging dataset 3 Dircadb, in order to verify its generalization ability. Although the imaging modality is different, CT images with weak boundaries and noise are relatively similar to IR images, in terms of visual quality, and pose challenges for segmentation. The results of segmentation on 3 Dircadb are shown in Figure 13, in which columns (a-f) are the results generated by SLIC, SNIC, LRW, USEAQ, CAS, and our method, respectively. In Example 1, all methods except for SLIC could correctly segment the tissue in the red region, as the size of this tissue was similar to the expected size of the superpixel, and the shape was nearly convex. When the size of the tissue to be segmented is much smaller than the expected size of the superpixel, it is difficult to achieve accurate segmentation, as shown in Figure 13, Example 2. In the blue and green rectangles of Figure 13, compared to other methods, only our method was able to extract the tissue entirely, even if it was small. The results of the experiment on 3 Dircadb provided clear support that the superpixels produced by our method had higher segmentation accuracy. Most existing algorithms generate a superpixel map based on the expected number of superpixels. A large expected number of superpixels can improve the boundary adherence, but over-segmentation will be aggravated, especially in smooth regions. Although our SSCE achieved better boundary adherence, based on applying adaptive seed sampling instead of increasing the number of superpixels in each interval, we deliberately added additional superpixels to the large initial homogeneous regions, in order to maintain the convex shape. Therefore, there may be unnecessary segmentation in smooth regions, as was seen throughout the experiments. Merging similar initial superpixels can reduce the occurrence of over-segmentation and increase semantics, as shown in Figure 14. Compared to the initial superpixel map, the refinement results in smooth regions, such as the sky, allowed us to use fewer superpixels to represent objects. As a result, SSCE can achieve higher boundary adherence with fewer superpixels. Figure 13: Experimental results on the medical image dataset 3 Dircadb: (**a**) result of SLIC; (**b**) result of SNIC; (**c**) result of LRW; (**d**) result of USEAQ; (**e**) result of CAS and (**f**) result of our method. Alternating rows show the segmented results and their local details. The spatial proximity weight and edge penalty term have a facilitating effect on boundary adherence. Ablation study about the spatial proximity weight and edge penalty term is shown in Figure 15. The spatial proximity weight can constrain the size of superpixel close to the seed sampling area by balancing the spatial similarity. Without the spatial proximity weight, seeds from small sampling area will expand the enclosing region when facing seeds from large sampling area as shown in the red box in Figure 15a. The spatial proximity weight can reasonably compress the size of superpixels generated by seeds from small sampling region, which can improve boundary adherence as shown in the red box in Figure 15b. However, when the pixel is close to the seed point, the impact of luminance similarity will decrease. Crossing the image boundary easily occurs near the edge. To correct the similarity evaluation, edge penalty term is introduced. From Figure 15c, the edge constraint can improve the capability of boundary adherence. For seeds from the same homogeneous region, both spatial proximity weight and edge penalty term have no significant effect, as shown in blue box in Figure 15. In summary, SSCE has better boundary adherence.