To avoid the creation of redundant key-frames, the video frames are grouped into different scenes based on Discrete Cosine Transform (DCT). Then, distinct visual features based on Histogram of Oriented Gradient (HOG) and Singular Value Decomposition (SVD) are extracted from each key-frame of every scenes of the videos of an intermediate candidate database and a query video for copy detection. The novelty of proposed method lies in reducing the computation overhead by generating an intermediate candidate database that are similar to the query video using ring-based Ordinal Measure (OM). In this paper, an effective and fast video copy detection method is presented by exploiting both spatial-temporal information to tackle the above-mentioned challenges. Moreover, there exist a trade-off between discriminability and robustness properties in most of the existing copy detection approaches. To mitigate the problem of computation overhead is still challenging in video copy detection. Most of the existing video copy detection approaches are robust against the content-preserving distortions such as brightness enhancement and compression, but less robust against the geometric distortions such as rotation and scaling. ![]() ![]() ![]() Visual hashing-based or fingerprinting-based video copy detection approach has been adopted numerously by the video search community due to significant escalation of manipulated copies of original videos over the Internet.
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