## 2 Overview of Related Works
### SBD Approaches  
A video contains a great amount of information at different levels in terms of scenes, shots, and frames. SBD is the first process to simplify applications, such as video indexing, retrieval, and fingerprinting. A shot is a set of continuous frames recorded by a single camera. SBD approaches can be divided into two categories: the compressed domain and the uncompressed domain. Some SBD algorithms are in the compressed domain and they are faster than those methods in the uncompressed domain. However, the uncompressed domain presents more challenges because of the vast amount of visual information in the video frames. Consequently, research on SBD is focused on the uncompressed domain rather than the compressed domain. In recent years, the SBD approach has made rapid progress. Table 3 lists some representative types of SBD approaches. The approaches are mainly in the following fields: pixel-based [30; 31; 32], histogram-based [33; 34; 35], edge-based [36; 37], transform-based [38; 39; 40], motion-based [41], and statistical-based [42; 43] approaches. The histogram-based method can be regarded as invariant to local motion or small global motion compared with the pixel-based methods. The edge-based method is simple to conduct. The processing of transform-based approaches usually transforms a signal (frame) from the time (spatial) domain into the transform domain. For motion-based approaches, the motion vectors are computed by block matching consecutive frame blocks. Transitions and camera operations, such as zoom or pan, can be differentiated. For statistics-based approaches, properties such as the mean, median, and standard deviation are often used. There are some other SBD approaches, such as the temporal slice coherency [44], fuzzy rules [45], and two-phased [46] approaches. Due to deep learning having become a hot topic in research work, deep-learning-based approaches have been increasingly applied to SBD works [47; 48; 49; 50]. Those methods have accordingly shown better performance than convolutional methods. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & **Presenters** & **Methods** & **Characteristics** \\ \hline \multirow{8}{*}{Pixel-based} & [PERSON] [30] & The method uses the sum of the absolute differences of the total pixel with a threshold to locate the cut shot. & Easy and fast, but cannot give a satisfactorily result. \\ \cline{2-4}  & [PERSON] [31] & The method uses the preprocessing method of average filtering before detecting shots. & False detection is the main problem. \\ \cline{2-4}  & [PERSON] [32] & The method divides the frame into regions, matches the regions between the current frame and its next frame, then chooses the best matches. & Real-time processing. \\ \hline \hline \multirow{8}{*}{Histogram-based} & [PERSON] [33] & The method uses fuzzy logic to generate a color histogram for SBD in the L’ab color space. & Robust to illumination changes and quantization errors, so it performs better than conventional color histogram methods. \\ \cline{2-4}  & [PERSON] [34] & The method uses the just-noticeable difference (JND) to map the RGB color space into three orthogonal axes JR, IG, and JB, and the sliding window-based adaptive threshold is used. & Highly depends on the size of the sliding window and parameter values. \\ \cline{2-4}  & [PERSON] [35] & The method uses a three-stage approach: the candidate shots are detected by two thresholds based on the sliding window at first; then, the local change and a cut change in to eliminate disturbances; finally, the HSV color space and Euclidean distance are employed. \\ \cline{2-4}  & & & \\ \cline{2-4}  & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Description of the SBD approaches.