# A Study of Eye-Tracking Gaze Point Classification and Application Based on Conditional Random Field
## 4 Experimental Results and Analysis
### Subjective Result Analysis  
Figure 11 presents the result of gaze points classification. The first row shows a visualization of the original gaze points. The second row shows the visualization result of gaze point classification by the proposed method.  
Figure 11: Result of gaze point classification. (**1**)–(**6**) is the number of the images, these number are used in Figure 12.  
Figure 10: The classification process of gaze point classification. (**a**) the gaze images; (**b**) the gaze points corresponding to each image; (**c**) the gaze point classification results.  
Statistical calculation and analysis are performed on the method of visual gaze points (Figure 11). The results of \(Ra\) and \(Co\) of gaze concentration, as well as the subjective ratio of the sample gaze point classification results are shown in Figure 12. It can be seen that the distribution of gaze points in different gaze images is different, and the concentration degree of gaze is also different.  
It can be seen from Figure 11 that there are many targets in the No. 1, No. 2, No. 3 gaze image, while the main target is clear, and the attention is relatively concentrated in the fixation process. In an example of image background watching a single task the main target is only one. When watching a gaze image in the process of test, there are 51 images of this type in the 100 images. Therefore, we analysis the concentrated attention is shown in Table 3, the result of scene image gazing with large target significantly, and average \(Ra\) and \(Co\) values after classification are analyzed by various methods, the gaze images and gaze points data are from our collect data. The proposed method \(Co\) = 16.44 is the lowest, \(Ra\) = 69.67% is higher than other five methods. In addition, the gaze points classification results reflect the well gaze attention.  
In Figure 11, No. 4, No. 5, No. 6 gaze images in the target are more than in other images. In general, the visual characteristics of prominent performance are observed. The attention during the gaze is more scattered, and gaze exists for each target. The No. 4 image background single gaze task has a main target for the third person. The No. 5 image in the background is complex, but the relatively visual distribution of clear gaze task has main target for the second person. The No. 6 image in the background complex target is not prominent, and the main target of the gaze task is the first person. There are 49 images of such type in 100 images, and a statistical analysis of the gaze attentional concentration is shown in Table 4 with related methods, the gaze images and gaze points data are from our collect data. The proposed method \(Ra\) = 49.67% is the highest, \(Co\) = 21.39 is lower than other five methods. In addition, the gaze points classification results reflect the well gaze attention.  
Figure 12: Subjective comparison of sample attention point classification results.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Clustering [10]** & **SVM [11]** & **RF [12]** & **HMM [14]** & **GHMM [13]** & **CRF [16]** & **Proposed Method** \\ \hline \(Ra\) & 86.47\% & 67.01\% & 61.63\% & 67.52\% & 62.66\% & 66.33\% & 69.67\% \\ \(Co\) & 19.21 & 16.98 & 20.03 & 17.48 & 19.87 & 17.52 & 16.44 \\ \hline \end{tabular}
\end{table}
Table 3: Result of scene image gazing with large target saliency.  
In general, the subjective result show that gazing images with different visual characteristics can have different effects on human attention and gaze results. The proposed method considers the human visual characteristics during gaze, which is more reasonable, and the classification of gaze points for targets is more relative with the physiological characteristics of people. Simultaneously, the results also showed that in such analysis of gaze point, the relative gaze point of concentration is high, and is relatively better for the gaze point interaction accuracy and stability.