## 4 Experimental Results and Analyses
### Experiment on Shot Boundary Detection  
#### 4.2.1 Open Video Scene Detection (OVSD) Dataset  
Here, we use the videos in the OVSD dataset [49] to compare our proposed method with the Filmora software [68]. The OVSD dataset is presented for the evaluation of scene detection algorithms and its shot boundary annotations are also given. The ground-truth scene annotations are provided by using a movie script. It consists of five short videos and a full-length film. Recently, a dataset extension, including 15 new full-length videos, has also been uploaded, but it only provides the scene annotations. Information on the OVSD dataset is listed in Table 9. Among the data, \"Bunny\" has a more vivid color than the other animated movies. Sample frames from the videos \"Big Buck Bunny\", \"Cosmos Laundromat\", and \"Sintel\" are shown in Figure 8.  
In Table 8, the test video name, numbers of manual shots, shots numbers detected by using the proposed method, shots numbers detected by using the Filmora software, and total frame numbers are listed. A comparison of the results of the test videos is also displayed in Table 10.  
The open access video editor Filmora offers an advanced feature that can automatically split a film into its basic temporal segments by detecting the transitions between shots in a video. From Table 10, it can be seen that our proposed SBD method is similar to Filmora and has a slightly lower miss rate. The number of Filmora-detected boundaries is not less than the frame rate of the video; however, our proposed method uses a step that is a quarter of the frame rate. Since the shot annotations of the video \"Big Buck Bunny\" and \"Cosmos Laundromat\" are created by a script, the accuracy of the shot location has some difference to the real frames. Therefore, we use the manual annotation of \"Big Buck Bunny\" (marked as 91*) instead of the annotation in the OVSD dataset. The video \"Leon\" has many more cut shots than gradual shots and the distance between them is larger than the frame rate, so the shot boundary is clear. The video \"Gangnam Style\" is a music movie, and it has many cut shots with a distance that is less than half the frame rate, so our method and Filmora are unable to detect those shot changes. Next, we use those videos' SBD to evaluate the performance of the proposed method. Due to the video \"Valkaama\" being too long, in the experiment, we only chose the second 10 min for the test. A boundary detected by the algorithm was said to be correct if it was within a quarter of the number of frame rate frames of a boundary listed in the baseline. Strictly speaking, the deviation for acceptance is \begin{table}
\begin{tabular}{c} \hline
**Video information** & **Shot Plot** \\ \hline Name: “Cosmos \\ Laundromat.mp4”; \\ Manual: 94; \\ Proposed: 104; \\ Filmora: 124; \\ Frames: 14,394 \\ \hline Name: “Leon.mp4”; \\ Manual: 26; \\ Proposed: 28; \\ Filmora: 24; \\ Frames: 3920 \\ \hline Name: “Gangnam Style.mp4”; \\ Manual: 108; \\ Proposed: 74; \\ Filmora: 70; \\ Frames: 3771 \\ \hline \end{tabular}
\end{table} 
Table 10: _Cont._a little higher because the cut shots happen only during two neighboring frames. However, considering the longer duration of gradual shots, such as dissolves, fade-ins, fade-outs, and wipes, in the proposed method, the smallest step of the shots is not less than a quarter of the number of frame rate frames, so the deviation for acceptance should be higher than that value. The results of SBD for the OVSD dataset are listed in Table 11.  
Table 11 shows that the shot boundaries of the videos \"Bunny\", \"Cosmos\", and \"Valkaama\" can be extracted because they have many cut shots. The video \"Elephants Dream\" is a 3D Computer-Generated Imagery (CGI) animated science fiction video, so most of the scenes in it are hard to match with a venue from a natural environment. This shows the weakness of our place network. The video \"Sintel\" is also a computer-animated film, but it has lots of action.