## 6 Results and Discussion
Table 5 exhibits the results after testing the last model retrained with the AIRTLab dataset and ambiguous samples from the RVLS/Pexels dataset on a new and unknown dataset (in this case, the SCVD dataset), which corresponds to the models developed in epoch 5 of the preceding phase. The classifier performance is relatively low, with the DenseNet121 model achieving the highest AUC value of 0.444. However, the SCVD dataset differs significantly from the AIRTLab and RVLS/Pexels datasets, as it contains more \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **Epoch** & **Recall** & **Specificity** & **AUC** & **PV** & **NPV** & **Size** \\ \hline \multirow{8}{*}{DenseNet121} & 0 & 0.862 & 0.922 & 0.892 & 16 & 12 & 3864 \\ \cline{2-8}  & 1 & 0.950 & 0.921 & 0.936 & 19 & 8 & 3891 \\  & 2 & 0.972 & 0.931 & 0.952 & 10 & 5 & 3906 \\  & 3 & 0.986 & 0.933 & 0.959 & 11 & 3 & 3920 \\  & 4 & 0.986 & 0.977 & 0.981 & 5 & 3 & 3928 \\  & 5 & 0.968 & 0.953 & 0.961 & 15 & 3 & 3946 \\ \cline{2-8}  & 0 & 0.624 & 0.354 & 0.489 & 47 & 19 & 2752 \\ \cline{2-8}  & 1 & 0.896 & 0.881 & 0.889 & 6 & 15 & 2773 \\  & 2 & 0.981 & 0.939 & 0.960 & 8 & 1 & 2782 \\ \cline{2-8}  & 3 & 0.965 & 0.984 & 0.974 & 2 & 4 & 2788 \\  & 4 & 0.978 & 0.984 & 0.981 & 5 & 3 & 2796 \\  & 5 & 0.995 & 0.969 & 0.982 & 2 & 1 & 2799 \\ \cline{2-8}  & 0 & 0.771 & 0.767 & 0.769 & 40 & 22 & 2785 \\ \cline{2-8}  & 1 & 0.886 & 0.980 & 0.933 & 4 & 14 & 2803 \\  & 2 & 0.991 & 0.962 & 0.976 & 12 & 5 & 2820 \\ MobileNetV2 & 3 & 0.978 & 1.000 & 0.989 & 0 & 4 & 2824 \\  & 4 & 0.991 & 0.940 & 0.965 & 20 & 3 & 2847 \\  & 5 & 0.978 & 1.000 & 0.989 & 0 & 9 & 2856 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Experimental result values on recall, specificity, and AUC metrics corresponding to the RVLS/Pexels dataset using our proposed active learning approach. These results were obtained in the second stage (Section 4.2). Figure 4: Some examples of images selected by the classifier as ambiguous or hard to classify. (**a**) and (**b**) display physical violence and non-physical violence scenes, respectively. diverse scenarios with more people in the frame and less defined scenes (see Figure 2). Despite these challenges, the process of retraining the classifier leads to an improvement in effectiveness, with AUC values ranging from 0.811 to 0.914. The number of ambiguous scenes selected by the model is higher compared to the other datasets (see Tables 3 and 4), indicating that these images are more difficult for the classifier to learn compared to those used previously (see Figure 2). Furthermore, the images selected as ambiguous in this phase are confusing for both the classifier and the human expert. Some examples of such ambiguous scenarios are depicted in Figure 5a,b. There is a tendency in recall and specificity to classify non-physical violence images better than physical violence ones, even though the distinction is unclear. The performance of the neural network models studied in this work could still be improved despite including a threshold active learning approach. Tables 3-5 show that there still exists a margin for improvement because all AUC values are lower than 1. In other words, the model misclassifies some images, in which it has no doubts about their  
Figure 5: Two examples of images selected by the classifier as ambiguous or hard to classify from the SCVD dataset. (**a**) and (**b**) display physical violence and non-physical violence scenes, respectively.