## 2 Overview of Related Works
### SBD Approaches
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & **Presenters** & **Methods** & **Characteristics** \\ \hline \multirow{6}{*}{Edge-based} & [PERSON] [36] & The method uses the Robert edge detector for gradual shot detecting; the fixed threshold is to determine the total number of edges that appear. & Fast but the performance for gradual transition detection is not good. \\ \cline{2-4}  & [PERSON] [37] & The method uses locally adaptive edge maps for feature extraction and uses three-level adaptive thresholds for video sequencing and shot detection. & Fast, uses an adaptive threshold, and is slightly superior to the color-based and histogram-based methods. \\ \hline \multirow{6}{*}{Transform-based} & [PERSON] [38] & The method computes the self-similarity between the features of each frame and uses the DCT to generate low-order coefficients of each frame color channel for a similarity matrix. & Competitive with seemingly simpler approaches, such as histogram differences \\ \cline{2-4}  & [PERSON] [39] & The method uses the Walsh–Hadamard operation to extract the edge strength frames. & Simple but the performance should be improved. \\ \hline \multirow{6}{*}{Motion-based} & [PERSON] [40] & The method uses camera and object motion to detect transitions and uses the average inter-frame correlation coefficient and block-based motion estimation to track image blocks. & High computational cost and has dissolve detection as a weakness. \\ \cline{2-4}  & [PERSON] [41] & The method estimates the dominant motion in an image represented by a two-dimensional (2D) affine model. & Applicable to MPEG videos. \\ \hline \multirow{6}{*}{Statistical-based} & [PERSON] [42] & The method uses the mean, standard deviation, and skew of the color moments. & Simple but has dissolve detection as a weakness. \\ \cline{2-4}  & [PERSON] [43] & The method uses SVD updating and pattern matching for gradual transitions. & Reduces the process and has good performance in both cut and gradual transition detection \\ \hline \multirow{6}{*}{Temporal Slice Coherency} & \multirow{2}{*}{[PERSON] [44]} & The method constructs a spatial-temporal slice of the video and analyzes its temporal coherency. & Capable of detecting various wipe patterns. \\ \cline{2-4}  & & Slice coherency is defined as the common rhythm shared by all frames within a shot. & \\ \hline \multirow{6}{*}{Fuzzy-rule-based} & \multirow{2}{*}{[PERSON] [45]} & The method calculates a localized fuzzy color histogram for each frame and constructs a feature vector using fuzzy color histogram distances in a temporal window. Finally, it uses a fuzzy inference system to classify the transitions. & Threshold-independent and has a weakness against gradual transition types. \\ \hline \multirow{6}{*}{Two-phased} & \multirow{2}{*}{[PERSON] [46]} & The first phase detects candidate dissolves by identifying parabolic patterns in the mean fuzzy entropy of the frames. The second phase uses a filter to eliminate candidates based on thresholds set for each of the four stages of filtration. & Has good performance for detecting dissolve transitions, uses lots of sub-stages, and is threshold-dependent. \\ \hline \multirow{6}{*}{Deep learning based} & \multirow{6}{*}{[PERSON] [49]} & The method implements three steps and uses CNN to extract the features of frames. The final decision is based on the cosine distance. & Suitable for the detection of both cut and gradual transitions boundaries; is threshold-dependent. \\ \cline{2-4}  & \multirow{2}{*}{[PERSON] [48]} & The method uses Siamese networks to exploit the visual and textual features from the transcript at first and then uses the clustering algorithm to resents. \\ \cline{2-4}  & & The method uses a support vector machine (SVM) & \\ \cline{2-4}  & & to merge the outputs of a three-dimensional (3D) CNN with the same labeling and uses a histogram-driven temporal differential & Exploits big data to achieve high detection performance. \\ \cline{2-4}  & & measurement to reduce false alarms of gradual transitions. & \\ \hline \multirow{6}{*}{[PERSON] [50]} & \multirow{2}{*}{The method extracts the features using the AlexNet and ResNet-152 model. The method uses local frame similarity and dual-threshold sliding window similarity.} & \multirow{2}{*}{Threshold-dependent.} \\ \cline{1-1} \cline{2-4}  & & & \\ \cline{1-1} \cline{2-4}  & & & \\ \cline{1-1} \cline{2-4}  & & & \\ \cline{1-1} \cline{2-4}  & & & \\ \cline{1-1} \cline{2-4}  & & & \\ \hline \end{tabular}
\end{table}