# Salient Object Detection via Fusion of Multi-Visual Perception
## 3 Motivation and Background
### Visual Perceptual Features  
The proposed method uses five different perceptual features, i.e., gradient feature, MSCN coefficients, the dark channel, saturation, and hue in the HSV color space. These perceptual features play an essential role in the human visual recognition process. The details of each perceptual feature are described as follows.  
**Gradient feature**: The human visual system recognizes objects by their edges. The higher the value in a gradient image, the closer to the edge observed by human vision. The gradient feature uses edge detection operators to extract texture information from images and represent it as finer object structures. In this work, a robust contrast operator [32] (_cvo_contrast value operator) was used for gradient feature extraction.  
**MSCN coefficients**: Due to the smooth transition of image regions, adjacent pixels of natural images tend to have high correlations. The human visual system detects salient objects after removing this high correlation. The MSCN coefficients are proven to reduce image region correlation [33]. Therefore, an MSCN coefficient feature map can decorrelate salient objects with their surroundings. The MSCN coefficients are calculated as follows:  
\[I_{MSCN}(i,j)=\frac{I_{gray}(i,j)-\mu(i,j)}{\sigma(i,j)+1}, \tag{1}\]  
\[\mu(i,j)=\sum_{k=-K}^{K}\sum_{l=-L}^{L}\omega_{k,l}I_{gray}(i+k,j+l), \tag{2}\]  
\[\sigma(i,j)=\sqrt{\sum_{k=-K}^{K}\sum_{l=-L}^{L}\omega_{k,l}\big{[}I_{gray}( i+k,j+l)-\mu(i,j)\big{]}^{2}}, \tag{3}\]where \(i\in\{1,2,\ldots,M\}\) and \(j\in\{1,2,\ldots,N\}\) are spatial indices. \(\omega\) is a 2D circularly symmetric Gaussian weighting function sampled to three standard deviations (\(K=L=3\)) and rescaled to unit volume. \(I_{gray}(i,j)\) is the gray version of a raw image \(I\).  
In addition, we find that the distribution of the salient object values tends to be concentrated in part of the MSCN coefficient feature value. So, the feature map is combined with the histogram to detect salient objects.  
**Dark channel**: Although the dark channel prior is first proposed in the direction of dehazing, some papers have studied its feature to detect salient objects. Refs. [34; 35] demonstrated that the dark channel prior could effectively suppress detection failures due to large background regions, foreground touching image boundaries, and the foreground and background sharing similar color appearances.  
The dark channel is a kind of statistical characteristic of outdoor haze-free images because the study found that in most non-sky patches, at least one color channel has some pixels with very low pixel intensities close to zero [36]. It is obtained as follows:  
\[I^{dark}{}_{(x)}=\min_{y\in\Omega(x)}(\min_{c\in\{r,g,b\}}I^{c}(y)). \tag{4}\]  
where \(I^{c}(y)\) is a color channel of \(I\) and \(\Omega(x)\) is a local patch centered at \(x\).  
**Saturation**: Saturation can represent how color density drops from the maximum value to 0. The purer the color, the higher the saturation, and high saturation equals high recognizability of colors.  
**Hue**: Hue is considered to be the primary characteristic of color. Changing the hue will lead to a more considerable difference than changing the same amount of saturation or value [11]. Therefore, color is also an important perceptual feature when human eyes focus on salient features.  
Five feature maps \(f_{m}(m=1,2,\ldots,5)\) with size \(M\times N\) are obtained from the original image, gradient feature \(f_{1}\), MSCN coefficients \(f_{2}\), dark channel \(f_{3}\), saturation \(f_{4}\), and hue \(f_{5}\). To improve the detection efficiency while preserving the effective information of the feature maps, the feature maps are downsampled to the maximum value. In this work, the downsampled feature maps \(\hat{f}_{m}\) were obtained by K-fold maximum downsampling with the size of \((\frac{M}{K})(\frac{N}{K})\). Then, the downsampled maps are normalized as follows:  
\[\hat{f}_{m}(i,j)=[\hat{f}_{m}(i,j)-\hat{f}_{m,\text{min}}]/(\hat{f}_{m,\text {max}}-\hat{f}_{m,\text{min}}). \tag{5}\]