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Part of the book series: Lecture Notes in Computational Vision and Biomechanics ((LNCVB,volume 30))

Abstract

An effective and exact technique for copying video location in a huge dataset is the utilization of video pictures. We have exactly picked the shading format description, a minimal and powerful casing-based description to make pictures which are additionally determined by vector quantization (VQ). We recommend a new nonmetric length measure to discover the likeness among the question and a dataset video picture and tentatively demonstrate its better execution over other length measures for exact copy identification. The effective look cannot be executed for high-dimensional information utilizing a nonmetric distance measure with accessible ordering systems. Consequently, we create a novel search algorithm in the view of precompiled distances and new dataset reduce systems yielding reduced recovery times. We perform different things with colossal dataset recordings. For singular questions with a normal span of 60 s (around half of the normal dataset video duration), the copy videos are recovered in 0.032 s, on Intel Xeon with CPU 2.33 GHz, with a high exactness of 98.5%.

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Correspondence to P. Karthika .

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Karthika, P., Vidhyasaraswathi, P. (2019). Digital Video Copy Detection Using Steganography Frame Based Fusion Techniques. In: Pandian, D., Fernando, X., Baig, Z., Shi, F. (eds) Proceedings of the International Conference on ISMAC in Computational Vision and Bio-Engineering 2018 (ISMAC-CVB). ISMAC 2018. Lecture Notes in Computational Vision and Biomechanics, vol 30. Springer, Cham. https://doi.org/10.1007/978-3-030-00665-5_7

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  • DOI: https://doi.org/10.1007/978-3-030-00665-5_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00664-8

  • Online ISBN: 978-3-030-00665-5

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