# An Enhanced Multimodal Stacking Scheme for Online Pornographic Content Detection
## 4 Experiment Results
### Analysis Results for Detection Time  
In addition, since it is important to rapidly detect harmful contents in a short time period for practical application in the online streaming environments, we compare the detection time and average content duration time of the component classifiers from the enhanced multimodal stacking scheme with those of the previous scheme used in [17], as shown in Table 5. In the proposed enhanced multimodal stacking scheme, the processing time of the first classifier, which is the fusion classifier, takes 0.13 s, the second video classifier takes 0.13 s (with a cumulative time of 0.26 s), and, finally, the audio classifier takes 0.04 s. A cumulative total time of 0.30 s, therefore, required. However, for the method in [17],  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{5}{c}{**True Positives**} & \multirow{2}{*}{\begin{tabular}{c} **False** \\ **Negatives** \\ \end{tabular} } \\ \cline{2-2} \cline{4-6}  & **1 st** & **2 nd** & **3 rd** & **4 th** & **Sum** & **Negatives** \\ \cline{2-6}  & **Classifier** & **Classifier** & **Classifier** & **Classifier** & **Classifier** \\ \hline \multirow{3}{*}{\begin{tabular}{c} Enhanced \\ stacking \\ \end{tabular} } & Fusion & Video & Audio & \multirow{3}{*}{None} & 1431/1500 & 69/1500 \\  & classifier & classifier & classifier & & (95.40\%) & (4.60\%) \\ \cline{1-1} \cline{2-6}  & 1398/1500 & 29/102 & 4/73 & & & \\ \cline{1-1} \cline{2-6}  & (93.20\%) & (28.43\%) & (5.47\%) & & & \\ \hline \multirow{3}{*}{\begin{tabular}{c} Previous \\ stacking [17] \\ \end{tabular} } & Video & Image & \multirow{3}{*}{\
\begin{tabular}{c} Motion \\ classifier \\ \end{tabular} } & Audio & \multirow{3}{*}{
\begin{tabular}{c} Audio \\ classifier \\ \end{tabular} } & 1415/1500 & 85/1500 \\  & classifier & & & (94.33\%) & (5.67\%) \\ \cline{1-1} \cline{2-6}  & 1162/1500 & 97/338 & 85/241 & 71/156 & \\ \cline{1-1} \cline{2-6}  & (77.47\%) & (28.70\%) & (35.27\%) & (45.51\%) & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of detection effects of the component classifiers.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Multimodal Ensemble Scheme** & **Accuracy** & **False Negative Rate** \\ \hline Enhanced multimodal stacking & 92.33\% & 4.60\% \\ Previous multimodal stacking used in [17] & 88.17\% & 5.67\% \\ Majority-voting ensemble & 84.30\% & 27.33\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Detection performance comparison of multimodal ensemble schemes.  
the processing time of the first classifier, which is the video classifier, takes 0.10 s, the second image classifier takes 0.55 s (for a cumulative time of 0.65 s), and the third motion classifier takes 0.50 s (for a cumulative time of 1.15 s). Finally, the processing time for the audio classifier takes 0.03 s, yielding a total detection time of 1.18 s. Here, since the image classifier first evaluates the harmfulness of each of the ten sampled frame images that comprise the segment and then determines the harmfulness by averaging the ten results, it requires more computation time. A long detection time duration is needed for the motion classifier because a great deal of preprocessing time is spent for extracting the horizontal and vertical movements of the video frames. If the weighted average processing time required for each content is calculated in conjunction with the number of harmful content detected by each component classifier shown in Table 4, the results are as shown in the rightmost column of Table 5. Since the detection time must be minimized in the online streaming environments, the enhanced multimodal stacking scheme proposed in this paper is comparatively superior in its performance because it detects harmful contents up to 0.88 s faster than the previous multimodal stacking scheme used in [17], thereby improving the detection time by up to 74.58%, and shortening the average detection time by 0.23 s at a rate of 62.16%.