Abstract
This chapter describes the agent based video contents identification scheme using watermark based filtering technique. To prevent a user from uploading illegal video contents into the WEB storages, two strategies are employed. First stage is the upload blocking of illegal contents including copyright ownership information as a watermark when a user tries to upload the illegal video content. Second stage is to monitor illegal video contents that are already uploaded. For this stage, the monitoring agent obtains video content link information, and then extracts the watermark from corresponding content using the Open API. For two stage video identification strategies, two types of watermark extraction schemes are employed. Gathered data obtained from agents is analyzed using data mining method, and reporting process is done. To show the effectiveness of the described system, some experimental evaluation and test are conducted.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
J. Haitsma and T. Kalker, “A highly robust audio fingerprinting system,” in Proc. Int. Conf. Music Information Retrieval, 2002.
P. Cano, E. Batlle, T. Kalker, and J. Haitsma, “A review of algorithms for audio fingerprinting,” in Proc. IEEE Workshop Multimedia Signal Processing, pp. 169–173, 2002.
B. Chor, A. Fiat, M. Naor, and B. Pinkas, “Tracing Traitors,” IEEE Trans. Inf. Theory, Vol.46, pp.893–910, May 200
M. Koster, “WWW Robots, Wanderers and Spiders,” URL: http://www.robotstxt.org/wc/robots.html
Digimarc Corporation, http://www.digimarc.com
J. Han, Y. Cai, and N. Cercone, “Data-Driven Discovery of Quantitative Rules in Relational Databases,” IEEE Trans. on Knowledge and Data Eng., vol. 5, pp. 29–40, 1993.
C. Bohm, S. Berchtold, and D. Keim, “Searching in high-dimensional spaces: Index structures for improving the performance of multimedia databases,” ACM Comput. Surv. Vol. 33, no. 3, pp. 322–373, 2001.
I. J. Cox, J. Killian, T. Leighton, and T. Shamoon, “Secure spread spectrum watermarking for multimedia,” Tech. Rep. 95–10, NEC Research Institute, 1995.
J. Oostveen, T. Kalker, and J. Haitsma, “Feature extraction and a database strategy for video fingerprinting,” in Proc. Int. Conf. Recent Adv. Vis. Inf. Syst., 2002, pp. 117–128.
Changick Kim and Bhaskaran Vasudev, “Spatiotemporal Sequence Matching for Efficient Video Copy Detection;” IEEE Trans. On Circuits and Systems for Video Technology, Vol.15, NO.1, pp.127–132, Jan. 2005.
Li Chen, and F.W.M. Stentiford, “Video sequence matching based on temporal ordinal measurement,” Pattern Recognition Letters, Vol.29, pp. 1824–1831, 2008.
Sunil Lee and Chang D. Yoo, “Robust Video Fingerprinting for Content-Based Video Identification,” IEEE Trans. On Circuits and Systems for Video Technology, Vol. 18, No.7, pp. 983–988, July 2008.
Androutsellis-Theotokis, S. and Spinellis, D.: A Survey of Peer-to-Peer Content Distribution Technologies, ACM Computing Surveys, Vol. 36, No. 4 (2004) 335–371.
V. Gorodetskiy, O. Karsaev, V. Samoilov, S. Serebryakov. P2P Agent Platform: Implementation and Testing. The AAMAS Sixth International Workshop on Agents and Peer-to-Peer Computing (AP2PC 2007), Honolulu, 2007 pp. 21–32.
V. Gorodetskiy, O. Karsaev, V. Samoilov, S. Serebryakov. Multi-Agent Peer-to-Peer Intrusion Detection. MMM-ACNS-2007. In series “Communication in Computer And Information Systems”, volume 1, Springer 2007, pp. 260–271.
C. Giannella, R. Bhargava, H. Kargupta, M. Klusch, J.C. Da Silva (2005): Distributed Data Mining and Agents. Journal of Engineering Applications of Artifical Intelligence, 18(4), Elsevier Science
H Kargupta, B Park, D Hershberger, E Johnson, “Collective data mining: A new perspective toward distributed data mining,” Advances in Distributed and Parallel Knowledge Discovery, 1999.
Longbing Cao, Chao Luo, Chengqi Zhang. Agent-Mining Interaction: An Emerging Area, AIS-ADM07, LNAI 4476, 60–73, Springer, 2007.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer Science+Business Media, LLC
About this chapter
Cite this chapter
Lee, H. (2009). Agent based Video Contents Identification and Data Mining Using Watermark based Filtering. In: Cao, L. (eds) Data Mining and Multi-agent Integration. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-0522-2_22
Download citation
DOI: https://doi.org/10.1007/978-1-4419-0522-2_22
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4419-0521-5
Online ISBN: 978-1-4419-0522-2
eBook Packages: Computer ScienceComputer Science (R0)