# NICE: Superpixel Segmentation Using Non-Iterative Clustering with Efficiency
## 4 Experiments and Analysis
### More Discussions on the Performance
Given \(\sigma=0.08\) for EIR in Table 4, the execution time on BSDS500 images can be reduced to 42 milliseconds which is approximately 40% faster than SNIC. To further verify the synergetic NICE, the performance is compared with SLIC, FLIC and SNIC on another benchmark dataset NYUv2 [23]. The dataset is converted by [7] for superpixel evaluation,  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Metrics**} & \multicolumn{8}{c}{**Parametric Value of \(\sigma\)**} \\ \cline{2-10}  & **0.01** & **0.02** & **0.03** & **0.04** & **0.05** & **0.06** & **0.07** & **0.08** & **0.09** & **0.10** \\ \hline BR & +0.0037 & +0.0032 & +0.0026 & +0.0021 & +0.0015 & +0.0  
which contains 399 test images with size \(608\times 448\). For fair comparison, the default parameters of original code released online is used in other three algorithms, while \(\sigma\) is still set to 0.03 in NICE. Figures 9 and 10 show the qualitative and quantitative performance of four methods on NYUv2, respectively. As shown in Figure 10, the iterative label updating methods SLIC and FLIC almost outperform the non-iterative label expansion methods SNIC and NICE on three metrics. Since the size of images of NYUv2 is larger than those in BSDS500, and the expected superpixel number \(K\) is limited in [50; 500], the average superpixel size in NYUv2 is greater than in BSDS500. Therefore, \(2s\times 2s\) search region for each seed in SLIC provides more context information than SNIC, which easily gets into local optimum with a step \(s\times s\) in grid initialization. Moreover, FLIC assumes that neighboring pixels have natural continuity, which is similar to inter-pixel correlation in EIR of NICE. Therefore, the active search strategy avoids the clusters being limited in fixed range in space, thus achieves more desirable boundary adherence. On the other hand, it results in less effect of compactness on controlling the regularity. As shown in Figure 9b, FLIC performs the worst in shape uniformity, which is too sensitive to maintain compact outlines among neighboring superpixels, even they all belongs to a homogeneous region (e.g., the floor and the wall). On the contrary, superpixels generated by SNIC and NICE are regular in both size and shape, while the former pays more attention to weak boundaries without apparent distortion. In addition, curves of metric on SLIC and FLIC tend to level off, whereas SNIC and NICE becomes better with the increasing of superpixel number. Figure 9: Visual comparison of superpixels with \(K=200\) on NYUv2. (**a**) SNIC; (**b**) FLIC; (**c**) SNIC; (**d**) NICE. Alternating columns show each segmented image followed by local details of each image. Table 5 shows the execution time (ET) of four algorithms. Owing to the fast convergence by active search strategy, FLIC runs over 50% faster than SLIC and becomes one of the fastest state-of-the-art methods. As a synergetic effect of AIR and EIR, NICE maintains stable acceleration of SNIC on NYUv2 that exceeds FLIC more than 20%. Thus, it is accepted that the proposed NICE makes a reasonable trade-off between performance and efficiency.