# A Study of Eye-Tracking Gaze Point Classification and Application Based on Conditional Random Field
## 4 Experimental Results and Analysis
### Objective Result Analysis  
Table 5 shows the comparison of gaze point classification results by different related algorithms in the example images of Figure 11. The proposed method outperforms the relevant algorithms in terms of precision, recall, and comprehensive performance. It can be seen from Table 5 that the overall performance of the proposed method is well and stable. The results of the image No. 1 in Figure 11 of the gaze points are classified using the clustering method in [10]. Although the various evaluation values are higher, because the fact that classification of the data is iteratively calculated in terms of the distance situation between the data, this method is simply considered from the distribution distance, and the results after visualization are clear (Figure 13), containing many gaze points that do not belong to the target range. The result of the image No. 3 in Figure 11 shows that too many gaze points of the target are classified (Figure 14), which instead makes the various evaluation values low. Similarly, the classification SVM method in [11] also has a relatively good classification result for gaze points. Although the data characteristics are \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 \(Ra\) & 32.16\% & 48.61\% & 40.31\% & 48.24\% & 48.37\% & 48.87\% & 49.67\% \\ \(Co\) & 19.38 & 23.55 & 22.73 & 21.14 & 23.56 & 21.42 & 21.39 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Result of scene image gazing with small target saliency. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & **1** & **2** & **3** & **4** & **5** & **6** \\ \hline \multirow{3}{*}{Clustering [10]} & _Precision_ & 89.38\% & 73.65\% & 56.21\% & 90.21\% & 76.53\% & 97.64\% \\  & _Recall_ & 96.31\% & 96.98\% & 97.02\% & 49.37\% & 95.32\% & 84.81\% \\  & _We_ & 89.91\% & 75.14\% & 58.23\% & 84.44\% & 77.79\% & 96.43\% \\ \hline \multirow{3}{*}{SVM [11]} & _Precision_ & 84.33\% & 85.78\% & 88.36\% & 83.59\% & 84.61\% & 86.17\% \\  & _Recall_ & 86.99\% & 85.92\% & 87.01\% & 84.04\% & 83.91\% & 85.06\% \\  & _We_ & 84.54\% & 85.79\% & 88.24\% & 83.62\% & 84.55\% & 86.07\% \\ \hline \multirow{3}{*}{RF [12]} & _Precision_ & 82.46\% & 83.61\% & 83.01\% & 82.86\% & 83.45\% & 84.26\% \\  & _Recall_ & 82.68\% & 83.85\% & 83.25\% & 82.96\% & 83.63\% & 85.24\% \\  & _We_ & 82.56\% & 83.73\% & 83.16\% & 82.91\% & 83.57\% & 84.69\% \\ \hline \multirow{3}{*}{HMM [14]} & _Precision_ & 86.33\% & 85.95\% & 62.06\% & 89.04\% & 84.19\% & 86.14\% \\  & _Recall_ & 85.77\% & 86.23\% & 63.54\% & 88.53\% & 84.96\% & 86.26\% \\  & _We_ & 86.28\% & 85.97\% & 62.17\% & 88.99\% & 84.25\% & 86.14\% \\ \hline \multirow{3}{*}{GHMM [13]} & _Precision_ & 85.79\% & 84.96\% & 63.99\% & 88.78\% & 84.16\% & 86.35\% \\  & _Recall_ & 85.65\% & 85.63\% & 63.65\% & 88.96\% & 84.31\% & 86.64\% \\  & _We_ & 85.71\% & 85.38\% & 63.78\% & 88.81\% & 84.27\% & 86.43\% \\ \hline \multirow{3}{*}{CRF [16]} & _Precision_ & 86.21\% & 85.52\% & 88.91\% & 88.76\% & 84.99\% & 86.28\% \\  & _Recall_ & 85.98\% & 85.48\% & 86.04\% & 89.03\% & 85.59\% & 85.71\% \\  & _We_ & 86.19\% & 85.51\% & 88.66\% & 88.78\% & 85.04\% & 86.23\% \\ \hline \multirow{3}{*}{Proposed method} & _Precision_ & 86.21\% & 85.99\% & 89.15\% & 89.27\% & 84.65\% & 96.58\% \\  & _Recall_ & 85.98\% & 86.17\% & 86.38\% & 89.41\% & 85.22\% & 86.95\% \\ \cline{1-1}  & _We_ & 86.19\% & 86.00\% & 88.91\% & 89.28\% & 85.61\% & 96.61\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of sample gaze test evaluation results. considered, the factor relationship between human visual characteristics is not considered. The proposed method is based on the ideological basis of research [14; 16; 24] for integrated research, which not only considers the feature establishment of gaze points, but also the relationship between features.