Ring decomposition based video copy detection using global ordinal measure features and local features

Autor: Arambam Neelima, Alongbar Wary
Rok vydání: 2020
Předmět:
Zdroj: Multimedia Tools and Applications. 79:8287-8323
ISSN: 1573-7721
1380-7501
DOI: 10.1007/s11042-019-08412-4
Popis: 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. 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. To mitigate the problem of computation overhead is still challenging in video copy detection. Moreover, there exist a trade-off between discriminability and robustness properties in most of the existing copy detection approaches. 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. 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). 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. To avoid the creation of redundant key-frames, the video frames are grouped into different scenes based on Discrete Cosine Transform (DCT). To further preserve the spatial-temporal information, the Temporally Informative Representative Image (TIRI) is used to generate each key-frame of every scenes of videos. The experimental result shows that the proposed method is more efficient and robust against various distortions which outperforms the state-of-the-art copy detection approaches.
Databáze: OpenAIRE
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