# Active Contours in the Complex Domain for Salient Object Detection
## 4 Experiment Analysis and Results
### Comparison with Recent State-of-the-Art Models for Active Contours  
We evaluate our algorithm qualitatively and quantitively on IMAGE_1, IMAGE_2 and IMAGE_3, as shown in Figure 4, and on real images by comparison with SDREL [50], RBS [51], the CV model [29], the LI model [52], and the DRLSE model [39]. The results show the illustration of initialization additionally, the initial contour, the final contour and the salient object. The iterations represented in Figures 5 and 6 describe the time of evolution, represented in the form of a curve. The comparison results in Figure 5 demonstrates that our algorithm has iterate only once for all the three images to detect initial and final contour of the object, while the other state-of-the-art models fails to detect boundary of the object in first iteration. Figure 6 also shows that our proposed algorithm has a very low computational cost when compared to the other methods. Figure 4: Initial contour, final contour and salient object results of RBS, Chan and Vese (CV), LI, DRSLE, SDREL, and our proposed algorithm ACCD_SOD. Our algorithm directly fitted during the initial stage, while the other state-of-the-art models failed during the initialization stage, which increased the computational cost. Figures 1, 4 and 7, show that our proposed algorithm initializes on the boundary of the object, while the other models fail to find the boundary during the initialization time. The initialization of the curve plays an important role in object detection. Next, we conduct experiments on real images. The proposed method is evaluated on the Microsoft research Asia (MSRA-1000) dataset [53]. MSRA-1000 is a traditional dataset for saliency detection and it is designed to promote research in image via open and metrics-based evaluation. The dataset contains 1000 images with accurate pixel wise ground truth saliency annotations. MSRA-1000 dataset images have a large variety in their content, background structures are essentially simplistic and smooth. The dataset available on line: [[https://mmcheng.net/msra10k/](https://mmcheng.net/msra10k/)]([https://mmcheng.net/msra10k/](https://mmcheng.net/msra10k/)). Our algorithm used fixed values of sigma \((\sigma=4)\) and alpha \((\alpha=0.4)\) throughout the comparison. We comparatively evaluate the salient region of our algorithm and compare it with four state-of-the-art models on the basis of four metrics, i.e., the rand index [54], global consistency, variation information (VOI) [55], and the F-measure. We select 886 images from the MSRA dataset. Table 1 shows the results. The methods that are involved in the comparison are SDREL [50], RBS [51], the CV model [29], the LI model [52], and the  
Figure 5: Comparison of our algorithm with five method on the basis of the number of iterations. Figure 6: Comparison of our algorithm with five methods on the basis of time in seconds. DRLSE model [39]. Our ACCD_SOD achieves the highest RI (higher is better), lowest GCE and VOI (lower is better), and highest F-measure. In terms of running time, our algorithm is faster than the state-of-the-art models. We also compare our ACCD-SOD algorithm with SDREL [50], LFE [40], and LI [52] on the six synthetic images given in Figure 7. The comparison results that are based on the number of iterations are summarized in Figure 8, with the evolutions time being summarized in Figure 9. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**METHOD.** & **RI** & **GCE** & **VOI** & **F-M** \\ \hline SDREL & 0.616 & 0.255 & 1.631 & 0.612 \\ RBS & 0.600 & 0.203 & 1.729 & 0.607 \\ CV & 0.603 & 0.199 & 1.958 & 0.567 \\ LI & 0.570 & 0.215 & 1.631 & 0.353 \\ DRLSE & 0.535 & 0.277 & 1.568 & 0.599 \\ OUR & 0.6219 & 0.185 & 1.546 & 0.634 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Statistical Results of Evaluation on the MSRA-1000 Dataset. Figure 7: The first row consists of the original images.