The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.12 - December (2010 vol.43)
pp: 28-35
Tiejun Huang , Peking University
Yonghong Tian , Peking University
Wen Gao , Peking University
Jian Lu , Shanda Interactive Entertainment
ABSTRACT
Encryption and watermarking are the most common techniques used to protect copyrighted multimedia content, but both have many limitations. Mediaprinting offers a reproducible and reliable alternative for digital rights management and related applications on the Internet.
INDEX TERMS
Digital rights management, Mediaprinting, Content identification, Content matching
CITATION
Tiejun Huang, Yonghong Tian, Wen Gao, Jian Lu, "Mediaprinting: Identifying Multimedia Content for Digital Rights Management", Computer, vol.43, no. 12, pp. 28-35, December 2010, doi:10.1109/MC.2010.356
REFERENCES
1. W. Zeng, H. Yu, and C.-Y. Lin eds., Multimedia Security Technologies for Digital Rights Management, Academic Press, 2006.
2. M. Bober, and P. Brasnett, "MPEG-7 Visual Signature Tools," Proc. 2009 IEEE Int'l Conf. Multimedia and Expo (ICME 09), IEEE Press, 2009, pp. 1540-1543.
3. J. Lu, "Video Fingerprinting for Copy Identification: From Research to Industry Applications," Proc. SPIE, vol. 7254, 2009; http://159.226.42.40/jiaoxue-MMF/2009VideoFingerprinting_SPIE-MFS09.pdf .
4. V. Monga and B.L. Evans, "Perceptual Image Hashing via Feature Points: Performance Evaluation and Tradeoffs," IEEE Trans. Image Processing, vol. 15, no. 11, 2006, pp. 3452-3465.
5. S. Lee and C.D. Yoo, "Robust Video Fingerprinting for Content-Based Video Identification," IEEE Trans. Circuits and Systems for Video Technology, vol. 18, no. 7, 2008, pp. 983-988.
6. J. Oostveen, T. Kalker, and J. Haitsma, "Feature Extraction and a Database Strategy for Video Fingerprinting," Proc. 5th Int'l Conf. Recent Advances in Visual Information Systems (VISUAL 02), LNCS 2314, Springer, 2002, pp. 117-128.
7. Z. Yang, W.T. Ooi, and Q. Sun, "Hierarchical, Non-Uniform Locality Sensitive Hashing and Its Application to Video Identification," Proc. 2004 IEEE Int'l Conf. Multimedia and Expo (ICME 04), vol. 1, IEEE Press, 2004, pp. 743-746.
8. K. Eshghi and S. Rajaram, "Locality Sensitive Hash Functions Based on Concomitant Rank Order Statistics," Proc. 14th ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining (KDD 08), ACM Press, 2008, pp. 221-229.
9. B. Kulis and K. Grauman, "Kernelized Locality-Sensitive Hashing for Scalable Image Search," Proc. 12th IEEE Int'l Conf. Computer Vision (ICCV 09), IEEE Press, 2009, pp. 2130-2137.
10. S. Baluja and M. Covell, "Learning to Hash: Forgiving Hash Functions and Applications," Data Mining and Knowledge Discovery, vol. 17, no. 3, 2008, pp. 402-430.
11. Y.-H. Kuo et al., "Query Expansion for Hash-Based Image Object Retrieval," Proc. 17th ACM Int'l Conf. Multimedia (MM 09), ACM Press, 2009, pp. 65-74.
12. H. Lejsek et al., "NV-Tree: An Efficient Disk-Based Index for Approximate Search in Very Large High-Dimensional Collections," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 31, no. 5, 2009, pp. 869-883.
8 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool