## 6 Results and Discussion
The objective is to evaluate the model's performance in scenarios with diverse characteristics. The RVLS/Pexels dataset, as described in Section 5.1, comprises confusing images in class \(PV\) and clearly non-physical violence images in class \(NPV\) (see Figure 2c,d). Despite being a small dataset, the model trained in epoch 5 of the first stage demonstrates robustness in classifying this dataset. The initial performance of the model in epoch 0 varies across different architectures. Forinstance, DenseNet121 achieves an _AUC_ value of 0.892, while EfficientNetB0's performance is notably lower, with an _AUC_ of 0.489. However, the classifier's performance is significantly improved upon retraining the model with ambiguous images (epochs 1 through 5). For instance, the performance of the EfficientNetB0 model in epoch 5 reaches an _AUC_ of 0.982, suggesting the effectiveness of the proposed approach in enhancing classification accuracy. Consider that the model was retrained using approximately less than 21% of the RVLS/Pexels dataset, and the performance was notably increased. DenseNet121 and MobileNetV2 are more robust models in dealing with unknown environments, and EfficientNetB0's performance was also improved. In terms of recall and specificity, Table 4 shows a similar trend to the first stage, where the models perform better in classifying scenes as physical violence than non-physical violence, but it is not clearly defined. Regarding ambiguous images (see Figure 4), it is possible to notice that the maximum number of images considered as ambiguous was 76 in this dataset (with MobileNetV2), i.e., about 21% of samples from the RVLS/Pexels dataset (see Table 1). In other words, the proposed approach uses fewer samples than traditional  
Figure 3: Some examples of images selected by the classifier as ambiguous or hard to classify in AlRTLab dataset. (**a**) and (**b**) display physical violence and non-physical violence scenes, respectively. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Epoch** & **Recall** & **Specificity** & **AUC** & **PV** & **NPV** & **Size** \\ \hline \multirow{8}{*}{DenseNet121} & 0 & 1.000 & 0.658 & 0.829 & 471 & 3 & 2400 \\ \cline{2-7}  & 1 & 0.988 & 0.846 & 0.917 & 154 & 54 & 2874 \\  & 2 & 0.993 & 0.793 & 0.893 & 282 & 20 & 3082 \\  & 3 & 0.973 & 0.907 & 0.940 & 139 & 33 & 3384 \\  & 4 & 1.000 & 0.738 & 0.869 & 280 & 0 & 3556 \\  & 5 & 1.000 & 0.813 & 0.907 & 237 & 11 & 3836 \\ \cline{2-7}  & 0 & 0.963 & 0.919 & 0.941 & 140 & 11 & 2400 \\ \cline{2-7}  & 1 & 0.969 & 0.937 & 0.953 & 52 & 19 & 2551 \\  & 2 & 0.993 & 0.793 & 0.893 & 13 & 20 & 2622 \\ EfficientNetB0 & 3 & 0.990 & 0.947 & 0.968 & 22 & 0 & 2655 \\  & 4 & 1.000 & 0.738 & 0.869 & 4 & 5 & 2677 \\  & 5 & 1.000 & 0.813 & 0.907 & 5 & 4 & 2686 \\ \cline{2-7}  & 0 & 1.000 & 0.926 & 0.963 & 111 & 2 & 2400 \\ \cline{2-7}  & 1 & 1.000 & 0.952 & 0.976 & 39 & 7 & 2513 \\  & 2 & 1.000 & 0.939 & 0.969 & 66 & 13 & 2559 \\ MobileNetV2 & 3 & 1.000 & 0.930 & 0.965 & 50 & 0 & 2638 \\  & 4 & 1.000 & 0.939 & 0.969 & 35 & 0 & 2688 \\  & 5 & 0.994 & 0.884 & 0.939 & 78 & 0 & 2723 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Experimental result values on recall, specificity, and AUC metrics corresponding to the AlRTLab dataset using our proposed active learning approach. These results were obtained in the first stage (Section 4.1). methods that mix different datasets to obtain the best performance, for example, the studies in Refs. [2; 16; 25]. In contrast, Table 4 does not exhibit a trend in terms of what class is more confusing for the classifier, as was observed in Table 3. Images selected by the classifier using the threshold \(\mu\) could be clearly defined as physical violence or non-physical violence by a human expert (see Figures 2c and 4b), but for the model this is not true. However, adding these ambiguous images increases the classifier's performance; in other words, using the threshold \(\mu\) helps the classifier to improve its effectiveness.