## 3 Materials and Methods  
### PlaceNet-Based SBD Method  
#### 3.1.1 Candidate Segment Selection  
Generally, a video sequence has a lot of non-boundary frames and several boundary frames. To reduce the computational complexity, selecting the candidate segment is the first step in our scheme. Here, we adopt nine different features for candidate segment selection. We use a short video \"Leon\" as a test to find out the difference between them. \"Leon\" has 3920 frames with a frame rate of 24 fps. In the experiment, we let the first frame be the keyframe of each quarter frame rate distance, and then used those features to compute the difference in consecutive keyframes. The feature values in each keyframe are normalized to a total of 1. The dimensionality of each feature and the total computing time for \"Leon\" are listed in Table 4. The keyframe differences of each feature are represented by the stem plot. The red star marks in each figure label the reference shot location. Comparing the results of these methods with respect to the computing complexity, the Red, Green, Blue (RGB) histogram method is faster than the others and the GIST method is the slowest. The Singular Value Decomposition (SVD)-based HOG and LBP methods are faster than the Hue, Saturation, Value (HSV) histogram method. The Dual-Tree Complex Wavelet Transform (DTCWT) [51] is slower than DWT. Some of the larger differences are not marked by using the edge method, so a lower threshold is needed. HOG-SVD and GIST show better discriminability than the transform-coefficients-based method. Among them, color-based methods, such as C-RGB and Local Binary Pattern (LBP)-SVD, are superior to the others not only in time reduction but also in discriminating the differences. However, the C-RGB method has its discriminatory power reduced for black and white documentary programs. Therefore, LBP-SVD has been chosen for candidate segment selection. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Processing** & **Length/Time** & **Frame Difference Plot** \\ \hline \multirow{4}{*}{C-RGB} & Using MATLAB’s & & \\  & IMHIST & 512/12.5 s & \\  & function & & \\ \hline \multirow{4}{*}{C-HSV} & Change to HSV & & \\  & space, each of & & \\  & them has eight & & \\  & bins & & \\ \hline \multirow{4}{*}{LBP-SVD} & Computing the & & \\  & Local Binary & & \\  & Pattern (LBP) & & \\  & then using & & \\  & singular value & decomposition & \\  & (SVD) & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The results of candidate segment selection by using different features on “Leon”. The threshold should be adaptive for candidate segment selection because the threshold levels are different for different videos. A lower threshold will lead to a higher recall value and a lower threshold will lead to a higher recall value and a lower threshold will lead to a higher recall value. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Processing** & **Length/Time** & **Frame Difference Plot** \\ \hline GIST & LMgist function & 512/82.6 s & \\ HOG-SVD & \begin{tabular}{c} Computing \\ HOG then \\ using SVD \\ \end{tabular}  & 31/13.6 s & \\ DWT & \begin{tabular}{c} Computing \\ HOG then \\ using SVD \\ \end{tabular}  & 32/13.2 s & \\ DWT & \begin{tabular}{c} 2D DWT, then \\ using the mean, \\ STD, and SV \\ \end{tabular}  & 32/13.2 s & \\ DTCWT & \begin{tabular}{c} 2D DTCWT, \\ then using the \\ mean, STD, and \\ SVD values \\ \end{tabular}  & 52/17.5 s & \\ DTCWT & \begin{tabular}{c} 2D DTCWT, \\ then using the \\ mean, STD, and \\ SVD values \\ \end{tabular}  & 52/17.5 s & \\ DTCWT & \begin{tabular}{c} 52D DTCWT, \\ then using the \\ mean, STD, and \\ SVD values \\ \end{tabular}  & 52/17.5 s & \\ DTCWT & \begin{tabular}{c} 2D DTCWT, \\ then using the \\ mean, STD, and \\ SVD values \\ \end{tabular}  & 52/17.5 s & \\ DTCWT & \begin{tabular}{c} 52D DTCWT, \\ then using the \\ mean, STD, and \\ SVD values \\ \end{tabular}  & 52/17.5 s & \\ DTCWT & \begin{tabular}{c} 52D DTCWT, \\ then using the \\ mean, STD, and \\ SVD values \\ \end{tabular}