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Videntifier: identifying pirated videos in real-time

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International Multimedia Conference archive
Proceedings of the 15th international conference on Multimedia table of contents

Augsburg, Germany

DEMONSTRATION SESSION: Demo session 2 table of contents

Pages: 471 - 472

Year of Publication: 2007

ISBN:978-1-59593-702-5

Authors

Kristleifur Dadason	Reykjavík University, Reykjavík, Iceland
Herwig Lejsek	Reykjavík University, Reykjavík, Iceland
Fridrik Ásmundsson	Reykjavík University, Reykjavík, Iceland
Björn Jónsson	Reykjavík University, Reykjavík, Iceland
Laurent Amsaleg	IRISA-CNRS, Rennes, France

Sponsors

ACM: Association for Computing Machinery
SIGGRAPH: ACM Special Interest Group on Computer Graphics and Interactive Techniques
SIGMULTIMEDIA: ACM Special Interest Group on Multimedia

Publisher

ACM New York, NY, USA

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Downloads (6 Weeks): 14, Downloads (12 Months): 69, Citation Count: 2

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ABSTRACT

With the proliferation of high-speed internet access and the availability of cheap secondary storage, movie piracy has become a major problem. This demonstration paper describes the Eff² Videntifier, a content-based system for large-scale automatic copyright enforcement of videos. The paper briefly describes the database and image processing techniques underlying the system. It also describes our proposed demonstration, which realistically simulate scenarios of copyright violations of movies.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	L.E.K. Consulting The Cost of Movie Piracy. http://www.mpaa.org/2006_05_03leksumm.pdf
	2	H. Lejsek, F. H. Ásmundsson, B. Th. Jónsson and L. Amsaleg. NV-Tree: An efficient disk-based index for approximate search in very large high-dimensional collections. Tech. Rep. RUTR-CS07001, Reykjavík University, 2007.
	3	F. H. Ásmundsson. NV-Network: A Distributed Architecture for High Throughput Image Retrieval. Master's thesis, Reykjavík University, August 2006.
	4	David G. Lowe, Distinctive Image Features from Scale-Invariant Keypoints, International Journal of Computer Vision, v.60 n.2, p.91-110, November 2004 [doi>10.1023/B:VISI.0000029664.99615.94]
	5	Herwig Lejsek , Fridrik H. Ásmundsson , Björn Thór Jónsson , Laurent Amsaleg, Scalability of local image descriptors: a comparative study, Proceedings of the 14th annual ACM international conference on Multimedia, October 23-27, 2006, Santa Barbara, CA, USA [doi>10.1145/1180639.1180760]
	6	A. Joly, O. Buisson and C. Frélicot. Content-based Copy Detection using Distortion-Based Probabilistic Similarity Search. IEEE Transactions on Multimedia, 9(2):293--306, 2007.

CITED BY 2

	Geert Willems , Tinne Tuytelaars , Luc Van Gool, Spatio-temporal features for robust content-based video copy detection, Proceeding of the 1st ACM international conference on Multimedia information retrieval, October 30-31, 2008, Vancouver, British Columbia, Canada
	Zi Huang , Heng Tao Shen , Jie Shao , Xiaofang Zhou , Bin Cui, Bounded coordinate system indexing for real-time video clip search, ACM Transactions on Information Systems (TOIS), v.27 n.3, p.1-33, May 2009