## 2 Overview of Related Works
### VCD Approaches
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Presenters/Year** & **Methods** & **Characteristics** & **Based Types** \\ \hline INRIA-LEAR [14]/08 & The method uses the SIFT features representing the uniform sampled query frames, uses K-means to generate the visual vocabulary, and uses hamming encoding to generate the candidate set of video segments. & Using keyframe classifiers for video processing to increase the performance. & Keyframes \\ \hline AT\&T team [15]/09 & The method uses SBD to segment query videos and reference videos at the same time. The first frame of the shot is taken as a keyframe. & Using preprocessing to remove and reduce bad effects of transitions. & SBD \\ \hline CRIM team [16]/09 & The method segments the video files into shots, uses SIFT for feature representation, the L1 distance is used for quantization, and Latent Dirichlet Allocation (LDA) is applied to discrete discriminants over matches. & The Bag of Words (BOW) model is used to eliminate the features that are not representative enough. & SBD \\ \hline PKU-IDM team [17]/10 & The method uses four detectors: two visual local features (SIFT and SURF), one global feature DCT, and one audio feature: Mel-frequency cepstrum coefficients (MFCCs). & A fusion of features: audio and video. & Keyframes \\ \hline [PERSON] [18]/11 & The method maps each video frame of the test to the closest query video frame firstly, and then moves the query over the test to find the test segment with the highest number of matching frames. & A fusion model reduces the false alarm rate. & Keyframes \\ \hline [PERSON] [19]/12 & The method uses two global features: a pyramid histogram of oriented gradients (PHOC) and GIST; their binary features are quantified by using the pairwise & Uses a sparse random projection method to encode the features & Keyframes \\ \hline [PERSON] [20]/13 & The method F-SIFT starts by estimating the dominant curl of a local patch and then geometrically normalizes the patch by flipping before the computation of SIFT. & Flip-invariant and can save on computational cost. & Keyframes \\ \hline [PERSON] [21]/14 & The method fuses models of spatial modalities and temporal modalities. The spatial fingerprint consists of DCT coefficients signs in local areas in a keyframe and the temporal fingerprints the temporal variances in local areas in consecutive keyframes. & A video shots features and adaptive modality fusion. & SBD \\ \hline [PERSON] [22]/15 & The method is based on local non-negative matrix factorization (LNMF), which is used for shot detection. The VCD has two-stage processing and the Hausdorff distance is used. & The robustness needs to be enhanced. & SBD \\ \hline [PERSON] [23]/16 & The method uses five scene frames from a video for video authenticity. & Can save storage space. & Keyframes \\ \hline [PERSON] [24]/17 & The method uses a combination of features: ORB, R\&F ((\)Resize and Flip), and a & Low-cost extraction of features and the synergy of lightweight video fingerprints. & Keyframes \\ \hline [PERSON] [25]/17 & The method uses the frame fisher vector and scene fisher vector for video segments. & The query image is compared directly against video clips in the database. & SBD \\ \hline [PERSON] [26]/17 & The method extracts the Convolutional Neural Network (CNN) features from AlexNet [27], VGGNet [28], and GoogleNet [29] first, and then uses the vector aggregation method to make codebooks. & Deep learning based features. & Keyframes \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Description of the video copy detection (VCD) approaches.