Robust video copy detection in large-scale TV streams using local features and CFAR based threshold

2016 ◽  
Author(s):  
Gokhan Ozbulak ◽  
Fatih Kahraman ◽  
Suleyman Baykut
Keyword(s):  
Large Scale ◽  
Local Features ◽  
Video Copy Detection ◽  
Copy Detection

Audio-visual large-scale video copy detection

2011 ◽  
Vol 88 (18) ◽  
pp. 3803-3816 ◽  
Author(s):  
Yang Liu ◽  
Changsheng Xu ◽  
Hanqing Lu
Keyword(s):  
Large Scale ◽  
Video Copy Detection ◽  
Copy Detection

scholarly journals Feature Re-Learning for Video Recommendation

2021 ◽  
Vol 9 (VI) ◽  
pp. 3143-3149
Author(s):  
Chanjal C
Keyword(s):  
Data Augmentation ◽  
Video Retrieval ◽  
Poor Performance ◽  
Training Data ◽  
Video Annotation ◽  
Video Copy Detection ◽  
Copy Detection ◽  
Augmentation Strategy ◽  
Ranking Loss ◽  
Video Recommendation

Predicting the relevance between two given videos with respect to their visual content is a key component for content-based video recommendation and retrieval. The application is in video recommendation, video annotation, Category or near-duplicate video retrieval, video copy detection and so on. In order to estimate video relevance previous works utilize textual content of videos and lead to poor performance. The proposed method is feature re-learning for video relevance prediction. This work focus on the visual contents to predict the relevance between two videos. A given feature is projected into a new space by an affine transformation. Different from previous works this use a standard triplet ranking loss that optimize the projection process by a novel negative-enhanced triplet ranking loss. In order to generate more training data, propose a data augmentation strategy which works directly on video features. The multi-level augmentation strategy works for video features, which benefits the feature relearning. The proposed augmentation strategy can be flexibly used for frame-level or video-level features. The loss function that consider the absolute similarity of positive pairs and supervise the feature re-learning process and a new formula for video relevance computation.

scholarly journals VIDEO COPY DETECTION UTILIZING THE LOG-POLAR TRANSFORMATION

2016 ◽  
pp. 8-13
Author(s):  
Daniel Reynolds ◽  
Richard A. Messner
Keyword(s):  
Data Reduction ◽  
Reduction Process ◽  
Video Data ◽  
Video Frame ◽  
Detection Accuracy ◽  
Video Copy Detection ◽  
Copy Detection ◽  
Frame Size ◽  
Detection Process ◽  
Processing Step

Video copy detection is the process of comparing and analyzing videos to extract a measure of their similarity in order to determine if they are copies, modified versions, or completely different videos. With video frame sizes increasing rapidly, it is important to allow for a data reduction process to take place in order to achieve fast video comparisons. Further, detecting video streaming and storage of legal and illegal video data necessitates the fast and efficient implementation of video copy detection algorithms. In this paper some commonly used algorithms for video copy detection are implemented with the Log-Polar transformation being used as a pre-processing step to reduce the frame size prior to signature calculation. Two global based algorithms were chosen to validate the use of Log-Polar as an acceptable data reduction stage. The results of this research demonstrate that the addition of this pre-processing step significantly reduces the computation time of the overall video copy detection process while not significantly affecting the detection accuracy of the algorithm used for the detection process.

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