A Survey of Content based Video Copy Detection using Big Data

Authors(2) :-Karthika P, P. Vidhya Saraswathi

Content based video copy is the method of detecting illegally copied videos by analyzing them and comparing with original content. It extracts options from the original videos and verifies whether a duplication happens or not by looking at the extricated highlights. A state-of-the-art system of video copy detection is evaluated on VCDB to illustrate the limitations of existing techniques. The task of partial copy detection in videos aims at finding if one or more segments of a query video have (transformed) copies in a large data set. Transformations like new views are specific to 3D videos and create the copy detection even tougher. The algorithmic program realizes the 3D fake video and evaluate deep learning options learned to severally train on a distinct data sets. In this paper dynamic looking for the partial video copy detection is used to seek out a lot of segments of a query video and reference video to look the huge scale data set.

Authors and Affiliations

Karthika P
Research Scholar, Department of Computer Applications, Kalasalingam University/Krishnan koil, Tamilnadu, India
P. Vidhya Saraswathi
Assistant Professor, Department of Computer Applications, Kalasalingam University/ Krishnan koil, Tamilnadu, India

Text Mining, Data Mining, frequency of words and text file

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Publication Details

Published in : Volume 3 | Issue 5 | May-June 2017
Date of Publication : 2017-04-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 114-118
Manuscript Number : ICASCT2519
Publisher : Technoscience Academy

Print ISSN : 2395-6011, Online ISSN : 2395-602X

Cite This Article :

Karthika P, P. Vidhya Saraswathi, " A Survey of Content based Video Copy Detection using Big Data", International Journal of Scientific Research in Science and Technology(IJSRST), Print ISSN : 2395-6011, Online ISSN : 2395-602X, Volume 3, Issue 5, pp.114-118, May-June-2017.
Journal URL : http://ijsrst.com/ICASCT2519

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