# Salient Object Detection via Fusion of Multi-Visual Perception
## 4 Methodology
### Single Feature Detection  
Humans have an inconsistent understanding of different features, so it is impossible to use the same detection method for each feature. According to the characteristics of each feature, different detection strategies are designed to acquire the different saliency maps of every single-feature. **Gradient feature:** Gradient values are usually large at the contours of salient objects, so gradient features are selected to detect salient objects. During gradient feature map detection, salient objects are obtained by finding salient object edges row by row. First, each row of data is put into a chart, and its peak points are found, likely to be the edges of salient objects. In this paper, peak points larger than \(\lambda_{1}\times Map_{max}\) are considered as the edges of salient objects, where \(Map_{max}\) is the maximum value of the whole map, and \(\lambda_{1}\) is set to 0.45. Then, salient regions are identified through those peak points. If the peak points are concentrated, the space between the concentrated peak points is a salient area. Finally, salient objects can be obtained according to the salient regions of each row. Figure 2 illustrates the gradient feature detection process. Figure 2b shows one row of gradient changes taken from Figure 2a. In addition, the peak points larger than \(\lambda_{1}\times Map_{max}\) with an asterisk are marked, and the salient region of this row is obtained. Figure 2c shows the saliency map obtained by gradient features. **MSCN coefficients:** By studying the characteristics of MSCN coefficients, we note a significant difference in the MSCN distribution between salient objects and the background, as shown in Figure 3.  
In this paper, it is defined as the statistical histogram as the discrete function:  
\[\mathrm{h}(\mathrm{r}_{k})=\mathrm{n}_{k}, \tag{6}\]  
where \(\mathrm{r}_{k}\) represents the value of level \(k\), and \(\mathrm{n}_{k}\) is the number of \(\mathrm{r}_{k}\) in the image. Then, the corresponding statistical histogram can be obtained through the feature map of MSCN coefficients as Equation (6). The histogram is normalized as follows:  
\[\mathrm{p}(\mathrm{r}_{k})=\frac{n_{k}}{(\frac{M}{K})(\frac{N}{K})}, \tag{7}\]  
where \(\mathrm{p}(\mathrm{r}_{k})\) estimates the probability that the value \(\mathrm{r}_{k}\) appears in the MSCN coefficient feature map. When the histogram distribution satisfies one of the three conditions, the value  
Figure 1: The framework of the proposed method. Figure 2: An example of the gradient feature detection process. (**a**) Gradient feature map. (**b**) One row of gradient changes. (**c**) Gradient saliency map. range greater than \(\lambda_{2}\) is chosen as the distribution range of salient objects. If none of the above three conditions are satisfied, the distribution range of salient objects is in a region less than \(\lambda_{2}\). The initial value of \(\lambda_{2}\) is set to 0.4. If the salient region obtained by the initial value is small, \(\lambda_{2}\) is changed to enlarge the salient region. The three conditions are as follows:  
1. \(\sum\limits_{k=0}^{3}\mathrm{p}(\tau_{k})>\lambda_{3}\), where \(\lambda_{3}=0.7\), means that more than 70% of the MSCN coefficient maps values are in the front four levels;
2. \(\max(n_{k})=n_{0}\) means that the first level is the maximum value of the statistical histogram;
3. The maximum value of the statistical histogram is in the first four levels, and \(\mathrm{p}(\tau_{k})<1-\lambda_{3}\) exceeds eight levels;  
Figure 4 shows some examples of MSCN coefficient detection, where the histogram distribution of the first image satisfies one of the above three conditions, so the range greater than \(\lambda_{2}\) is the salient object distribution range. The other images do not meet the three conditions, so the range less than \(\lambda_{2}\) is the salient object distribution range. Figure 4c shows the detection result of the image.