# Salient Object Detection via Fusion of Multi-Visual Perception
## 5 Experiments
### Results  
The results of the proposed method for ECSSD and MASR10K datasets were compared with those of MSS [40], COV [41], GR [42], PCA [43], LDS [44], FCB [45], CNS [26], MDF [46], PPMRF [47], saliency bagging [2], MTL-ITSOD [48], PDLSO [7], and LC-SA [6] methods. These algorithms were selected to demonstrate the performance of our proposed method. The methods of Refs. [40,41] can combat complex natural scenarios and [42,43], based on the prior theory, make results more robust. Others [26; 46; 47] are representative methods that emerged in recent years. All these methods utilize color and some prior theories to suppress background information and highlight salient objects. The parameters settings in our work are shown in Table 1.  
The visual comparison of all evaluated methods is shown in Figure 8. The saliency maps generated by our method are more consistent with the ground truth. Our method can better suppress background noise and highlight salient objects.  
In Figure 9, saliency maps are evaluated through PR and ROC curves to show the performance of our method. The curve of our method is above that of other methods, which shows that our method leads the way compared with others.  
Moreover, to further clarify the advantages of our approach relative to existing methodologies, we performed comparisons based on quantitative metrics (F-measure and MAE) with the MSS [40], COV [41], GR [42], PCA [43], LDS [44], FCB [45], CNS [26], MDF [46], PPMRF [47], saliency bagging [2], MTL-ITSOD [48], PDLSO [7], and LC-SA [6] methods using the ECSSD and MSRA10K datasets. Notably, due to the absence of results for the MTL-ITSOD and PDLSO algorithms for the MSRA10K dataset and the lack of results for the LC-SA algorithm for the ECSSD dataset, these instances are indicated in the table with a hyphen. Analysis of the MAE and F-measure results from Table 2 reveals commendable performance of our proposed method for both the ECSSD and MSRA10K datasets.  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Parameter** & **Explanation** \\ \hline \multirow{2}{*}{\(K\)} & Downsampling factor of the feature map. Different sizes of images use different downsampling factors. The image size in this paper is 300 \(\times\) 400, using 4 times downsampling. \\ \hline \multirow{2}{*}{\(\lambda_{1}\)} & Threshold parameter when the gradient feature judges the gradient value of the salient object edge. It is set to 0.45. \\ \hline \multirow{2}{*}{\(\lambda_{2}\)} & The critical value of the region where the salient objects of the MSCN variance feature are located. It is initially set to 0.4, but when the salient region has few values, \(\lambda_{2}\) will dynamically change to enlarge the salient region. \\ \hline \multirow{2}{*}{\(\lambda_{3}\)} & Probability, used to estimate the probability of \(\tau_{k}\) appearing in the MSCN coefficient feature map. It is set to 0.7. \\ \hline \multirow{2}{*}{\(\lambda_{4}\)} & The parameter in threshold \(T\) used to adjust variance weights. It is set to 1.5. \\ \hline \multirow{2}{*}{\(\lambda_{5}\)} & The parameter used when denoising salient objects by density value. It is set to 0.45. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Setting parameters and explanation.  
Figure 7: Ablation experiments on multi-visual perception features for ECSSD.