# Salient Object Detection via Fusion of Multi-Visual Perception
## 4 Methodology
### Fusion with Superpixel Map  
For individual images, feature maps are determined based on the histogram distributions of different features. After obtaining feature maps through features, these feature maps are fused. Algorithm 1 shows the feature salient object fusion process. There are some non-salient points in each feature saliency map. Therefore, for accurate detection, we fuse the multi-perception features to calculate the final accurate feature map. It first calculates the density of salient points around each salient point of each feature saliency map \(\hat{f}_{m}\). The specific calculation method draws a rectangle centered on the point, calculates the salient point density in the rectangle, and obtains the salient density. After obtaining the saliency density of each saliency point, the saliency points whose saliency density is less than \(\lambda_{5}\times maximum\)_density_ are regarded as noise points and removed, and \(\lambda_{5}\) is set to 0.45 as the best value obtained from experiments. Then, the feature saliency maps after denoising are fused as the fused saliency map \(\hat{f}\). For any given image, the probability of meeting the foreground conditions using these three features is higher for the foreground. Therefore, we have assigned an equal weight ratio of 1:1 to each feature. Ultimately, the identification of three or more features as meeting the foreground criteria defines this area as the foreground. Then, the fusion process involves adding the values of the five feature saliency maps, where pixels with a sum greater than or equal to 3 are considered foreground, while those below 3 are considered background. Ultimately, the resulting saliency map is represented as \(\hat{f}\). Finally, an inverse up-sampling process is presented on the fused saliency map, restoring it to the input image size. The final fused saliency map of Figure 6a is shown in Figure 6b as one typical example. As can be seen from Figure 6b, the edges of salient objects are relatively rough, so the superpixel segmentation algorithm is introduced for refinement. Here, the SLIC superpixel segmentation algorithm [37; 38] is involved in removing the interference area to obtain the final salient object. In this paper, the fusion strategy of SLIC is improved by considering both the color and area of superpixel blocks when fusing superpixel blocks. As before, the SLIC algorithm is first used to segment the image [38]. Then, the blocks are fused with a small color difference, and the area of the fused blocks is judged. If the area of the block is too small, it is fused with the surrounding superpixel block with the smallest chromatic aberration. After each superpixel segmentation block is processed through the above steps, the final superpixel segmentation map can be obtained. Figure 6c is the final superpixel segmentation map of Figure 6a. Figure 5: An example of the hue detection process. (**a**) Hue statistics histogram. (**b**) Hue edge statistics histogram. (**c**) Hue growth result. (**d**) Hue feature map. (**e**) Hue saliency map. ```
1:Input: Each feature saliency map \(\hat{f}_{m}\)
2:Output: The fused saliency map \(\hat{f}\)
3:\(m\gets 1\)
4:\(//m\) is initialized to \(1\) and fused from the first feature map until the five feature maps are fused
5:while\(m\leq 5\)do
6:\(\mathrm{dens}(i,j)=\mathrm{Density}(\hat{f}_{m}(i,j))\);
7:\(//\)Calculate the surrounding salient point density for each point in \(\hat{f}_{m}\);
8:while\(\mathrm{dens}(i,j)<\lambda_{5}\times\mathrm{max}(\mathrm{dens})\)do
9:\(\hat{f}_{m}(i,j)=0\);
10:\(//\)Remove isolated salient points
11:endwhile
12:\(\hat{f}=\hat{f}+\hat{f}_{m}\);
13:\(m\gets m+1\);
14:endwhile
15:return\(\hat{f}\)
```  
**Algorithm 1** Multi-perception feature map fusion  
The final superpixel segmentation map splits the image into many superpixel blocks.