# A Study of Eye-Tracking Gaze Point Classification and Application Based on Conditional Random Field
## 2 Proposed Method
### Expression of Image Saliency Feature  
The human eye gaze at image contains rich feature information such as color, texture and structure. Different description methods of image feature have different advantages and disadvantages. In this paper, multiple feature factors are efficiently fused to describe the image target in order to enhance the robustness of visual feature descriptions of different images [28].  
With the dominant role of objective human task, the saliency of the target task in the images is prior to other elements. However, the human attention is also affected by the image local contrast, target edge region and center bias prior [29, 30, 31], which affects the final output gaze point. An example of images saliency for different methodological visual characteristics are shown in Figure 2. The first column in the image of local contrast factor efficiently distinguishes the target region. The second and fourth columns of the images distinguish the target region relative to the background prior factor, and the fifth column of the images distinguishes the target region significantly relative to the center bias  
Figure 1: The process of gaze point classification.  
factor. However, in the third column of the images, the three methods used to distinguish the target \"person\" are not outstanding. However, the bright part of the image clearly distinguished, and the local contrast is relatively distinct from the target on the right side of the image.  
Different salient features are used to represent the salient regions of the image [15; 32; 33]. This paper uses three color spaces (RGB, Lab and HSV), and extracts three features as image saliency features. Table 1 shows the target region pixel feature representation.  
The characteristics of target area \(p\) in gaze image \(I\) are expressed as:  
\[m_{p}(i,j)=\Big{\{}m_{p_{-}R},m_{p_{-}G},m_{p_{-}B},m_{p_{-}L},m_{p_{-}a},m_{p_ {-}b},m_{p_{-}H},m_{p_{-}s},m_{p_{-}V}\Big{\}}, \tag{2}\]  
where \(m_{p}(i,j)\in\mathbb{R}^{c},(i,j)\) is the pixel position of the target area, and \(c\) is the feature dimension.  
Figure 3 shows the specific representation of the pixel features \(m_{p}\) of the target area.  
Initially gazing at the image, the image saliency region feature is \(G_{0}^{s}=\text{max}m_{p}(i,j)\), the \(28\times 28\) image block feature centered at \((i,j)\) in the target region can be extracted to express, where pixel \((i,j)\) in target \(p\) area is the most attractive target region, then the saliency feature of the next moment image saliency region will be expressed as a \(28\times 28\) image block feature centered at before gaze point in the target region.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature** & **Definition** & **Dimension** \\ \hline RGB & Average RGB value of target area & 3 \\ Lab & Average Lab value of target area & 3 \\ HSV & Average HSV value of target area & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Pixel features per target area.  
Figure 3: Specific representation of the pixel features of the target area.  
Figure 2: Example of visual characteristics saliency cues expression by different methods.