Abstract
Multimedia content identification methods create a compact bitstream representation of the underlying content that is robust against common signal processing operations while being sensitive to the content. The robustness and sensitivity of the bitstream representation are conflicting requirements. In this chapter, we examine three issues in the context of achieving the tradeoff between robustness and sensitivity. They are (i) the representation domain for content (spatial, time or transform), (ii) local versus global features in the representation domain, (iii) robust hash of features (the first two of these directly relate to multimedia content analysis). We review the algorithms proposed in literature with these three issues in mind. Finally, we present some applications of content identification technology that exist today in the market and discuss the remaining challenges for future applications.
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References
E. Allamanche, J. Herre, O. Hellmuth, B. Bernhard and M. Cremer, “Audio id:towards content based identification of audio material,” 100th AES Convention, 2001.
S. Baluja and M. Covell, “Audio fingerprinting: Combining computer vision and data stream processing,” Proc. of ICME, 2007.
E. Batlle, J. Masip and E. Guaus, “Automatic song identification in noisy broadcast audio,” Proc. of SIP, Aug 2002.
M. Brand “Fast Low Rank Modifications of the Thin Singular Value Decomposition,” Linear Algebra and its Applications, pp. 20-30, 2006.
C.J.C. Burges, J.C. Platt, and S. Jana, “Distortion discriminant analysis for audio fingerprinting,” IEEE Transactions on Speech and Audio Processing, May 2003.
E. Cohen et al., “Finding Interesting Associations without Support Pruning,” Knowledge and Data Engineering, Vol. 13, pp. 64–78, 2001.
B. Coskun, B. Sankur, and N. Memon, “SpatioTemporal Transform Based Video Hashing,” IEEE Transactions on Multimedia, Vol. 8, no. 6, pp. 1190–1208, 2006.
D. Fragoulis, G. Rousopoulos, T. Panagopoulos, C. Alexiou and C.Papaodysseus, “On the automated recognition of seriously distorted musical recordings,” IEEE Transactions on Signal Processing, 2001.
J. Fridrich and M. Goljan, “Robust hash functions for digital watermarking,” ITCC, 2000.
J. Haitsma and T. Kalker, “A highly robust audio fingerprinting system,” Proc. of ISMIR, 2002.
M. Johnson and K. Ramchandran, “Dither-based Secure Image Hashing Using Distributed Coding,” Proc. of ICIP, 2003.
C. Kailasnathan, and R.S. Naini, “Image authentication surviving acceptable modifications using statistical measures amd k-mean segmentation,” IEEE-EURASIP Work Nonlinear Sig and Image Processing, Vol. 1, 2001.
Yan Ke, Derek Hoiem and Rahul Sukthanker, “Computer vision for music identification,” CVPR, 2005.
C. Kim, “Content-based image copy detection,” Signal Processing: Image Communication, vol. 18, pp. 169–184, 2003.
S. Kim and C. D. Yoo, “Boosted binary audio fingerprint based on spectral subband moments,” ICASSP, 2007.
J. Kornblum “Identifying Almost Identical Files Using Context Triggered Piecewise Hashing,” Digital Investigation, 3(S):91–97, Proceedings of the Digital Forensic Workshop, 2006.
S.S. Kozat,R. Venkatesan, and M.K. Mihcak, “Robust perceptual image hashing via matrix invariants,” Proc. of ICIP, 2004.
F. Lefbvre, J. Czyz and B. Macq, “A robust soft hash algorithm for digital image signature,” Proceddings of European Signal Processing Conference, 2002.
C.Y. Lin, and S.F. Chang, “A Robust Image Authentication Method Distinguishing JPEG Compression from Malicious Manipulation,” IEEE Transactions on Circuits and Systems for Video Technology, Vol. 11, no. 2, pp. 153–161, 2001.
C.Y. Lin and S.F. Chang, “A robust image authentication method distinguishing jpeg compression from malicious manipulation,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 11, no. 2, pp. 153–168, 2001.
D.G. Lowe, “Distinctive image features from scale invariant keypoints,” IEEE Journal on Computer Vision, 2004.
C. Lu, C.Y. Hsu, S.W. Sun and P.C. Chang, “Robust mesh based hashing for copy detection and tracing of images,” Proc of ICME, 2004.
S. Mavandadi, and P. Aarabi, “Rotation Invariance in Images,” Proc. of ICASSP, 2007.
M.K. Mihcak and R. Venkatesan, “New iterative geometric methods for robust perceptual image hashing,” Proc. of ICIP, 2000.
M.K. Mihcak and R. Venkatesan, “New iterative geometric methods for robust perceptual image hashing,” Proceedings of ACM Workshop on Security and Privacy in Digital Rights Managment, 2001.
M.K. Mihcak and R. Venkatesan, “A perceptual audio hashing algorithm: A tool for robust audio identification and information hiding,” ICIP, 2004.
M.L. Miller, M.A. Rodriguez, and I.J. Cox, “Audio Fingerprinting: Nearest Neighbor Search in High Dimensional Binary Spaces,” Journal of VLSI Signal Processing, Vol. 41,pp. 285–291, 2005.
V. Monga and B. L. Evans, “Robust perceptual image hashing using feature points,” Proc. of ICIP, 2004.
V. Monga, A. Banerjee, B.L. Evans “A Clustering based Approach to Perceptual Image Hashing,” IEEE Transactions on Information Forensics and Security, Vol. 1, no. 1, Mar 2006.
V. Monga, M.K. Mihcak “Robust and Secure Image Hashing via Non-Negative Matrix Factorizations,” IEEE Transactions on Information Forensics and Security, Vol. 2, no. 3, Sep 2007.
H. Nicholas “DCFLDD Defense Computer Forensics Lab, http://dcfldd.sourceforge.net/” 2002.
J. Oostveen, T. Kalker and J. Haitsma, “Visual Hashing of Digital Video:Applications and Techniques,” Proc. of ACM Multimedia, New York, 2004.
H. Ozer, B. Sankur and N.D. Memon, “Robust audio hashing for audio identification,” Proc. of ICME, 2007.
R. Radhakrishnan, C. Bauer, C. Cheng and K. Terry, “Audio signature extraction based on projections of spectrograms,” Proc. of ICME, 2007.
R. Radhakrishnan, C. Bauer “Robust Video Fingerprints based on Subspace Embedding,” Proc. of ICASSP, 2008.
R. Radhakrishnan and C. Bauer, “Content-based video signatures based on projections of difference images,” IEEE Proc. on MMSP, 2007.
M. Schneider and S.F. Chang, “A robust content-based digital signature for image authentication,” IEEE Proc. on ICIP, 1996.
J.S. Seo, J. Haitsma, T. Kalker, and C.D. Yoo, “A robust image fingerprinting system using the radon transform,” Signal Processing:Image Communication, vol. 19, pp. 325–339, 2004.
A. Swaminathan, Y. Mao, and M. Wu, “Image hashing resilient to geometric and filtering operations,” Proc. of MMSP, 2004.
A. Tridgell “Spamsum README from: http://samba.org/ftp/unpacked/junkcode/spamsum/readme” 2002.
R. Venkatesan, S.-M. Koon, M.H. Jakubowski and P. Moulin, “Robust image hashing,” Proc. of ICIP, 2000.
A. Wang, “Shazam,” CVPR, 2005.
S. Xiang, H.-J. Kim, and J. Huang, “Histogram based image hashing scheme robust against geometric deformations,” Proceedings of ACM Workshop on Multimedia and Security, 2007.
S.-H. Yang, and C.-F. Chen, “Robust Image Hashing based on SPIHT,” Proc. of ITRE, 2005.
P.N. Yianilos “Locally Lifting the Curse of Dimensionality for Nearest Neighbor Search,” Proc. of 11th Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 361–370, 2000.
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Radhakrishnan, R., Memon, N. (2009). Multimedia Analysis for Content Identification. In: Divakaran, A. (eds) Multimedia Content Analysis. Signals and Communication Technology. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-76569-3_10
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DOI: https://doi.org/10.1007/978-0-387-76569-3_10
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