# Infrared Image Superpixel Segmentation Based on Seed Strategy of Contour Encoding
## 4 Experiment
### Results
However, SLIC, SNIC, and LRW tended to produce superpixels with similar sizes and remained regular in complex texture regions, thus reducing the boundary adherence capability, especially in weak regions (e.g., the edge of the mountain in Example 2 of Figure 11). In contrast, USEAQ and CAS had better boundary adherence performance, as shown in Figure 11d,e, but their results were irregular. In Example 2 of Figure 11, both of them showed dentate and sinuous boundaries, and USEAQ achieved better boundary adherence at the cost of higher irregularity. Since our method places seed points according to the image content and the sampling interval is only used to maintain convexity, our method was able to maintain both regularity and better boundary adherence, as can be observed in Figure 11f. In the smooth regions, our method achieved compactness similar to that of SLIC, SNIC, and LRW, but the superpixels generated by our method could better extend along the edge of local objects. The amplified details in Figure 11 showed that only our method could completely segment the small \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **SLIC** & **SNIC** & **LRW** & **USEAQ** & **CAS** & **SSCE** \\ \hline IR Dataset & 0.116 & 0.143 & 148.515 & 0.099 & 0.253 & 0.216 \\ \hline BSD500 & 0.067 & 0.075 & 72.913 & 0.060 & 0.138 & 0.134 \\ \hline
3 Dircadb & 0.083 & 0.113 & 128.134 & 0.076 & 0.188 & 0.177 \\ \hline Average & 0.089 & 0.110 & 116.520 & 0.078 & 0.193 & 0.176 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Running times of different methods on three datasets (in seconds). Figure 11: Experimental results of IR image superpixels in the self-built dataset from compared algorithms: (**a**) SLIC; (**b**) SNIC; (**c**) LRW; (**d**) USEAQ; (**e**) CAS; and (**f**) our method. The zoomed-in regions located below the results show that our method has better capability of adhering to boundaries. objects, such as the road, bush, and aircraft, without increasing the expected number of superpixels. In summary, our proposed segmentation algorithm based on a seed sampling strategy improved the boundary adherence capability while maintaining regular shapes in the self-built IR dataset. In order to validate the feasibility of our method on gray images and illustrate the strengths of our algorithm, our algorithm and the compared methods were tested on the most commonly used segmented dataset BSD500. The results presented in Figure 12 lead to similar conclusions as those from the experiment on the self-built IR dataset: our method maintained regular shapes in smooth regions and better adhered to boundaries in complex textured regions. As shown in Figure 12, the superpixels generated by SLIC, SNIC, LRW, and our method maintained smooth and regular shapes in smooth regions such as the sky; in comparison, those obtained with USEAQ and CAS were slightly less regular. However, in highly textured regions, the irregular shapes produced by USEAQ and CAS were more pronounced, with severe dentate boundaries, such as the zoomed-in stone in Figure 12, Example 3. Although SLIC, SNIC, and LRW remained compact, the boundary adherence capability was weakened, which can be observed in the aircraft tail in Figure 12, Example 1. Compared with the other methods, our method had better boundary adherence performance. Only our method was able to generate superpixels along the boundary of the caudal wing, and the letter \"A\" was also precisely segmented. We can observe, from the results, that when a low expected number of superpixels was set to ensure that no over-segmentation occurs in smooth regions, our method could appropriately increase the number of superpixels in the complex textured regions, according to the image content, to achieve correct segmentation. Therefore, our method is more advantageous, in terms of boundary tracking. Figure 12: Visual comparison of superpixel segmentation among six different methods tested on the BSD500 dataset.