Advertisement

Multimedia Analysis for Content Identification

  • Regunathan Radhakrishnan
  • Nasir Memon
Chapter
Part of the Signals and Communication Technology book series (SCT)

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.

Keywords

Input Image Geometric Attack Input Feature Vector Coarse Representation Multimedia Analysis 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    E. Allamanche, J. Herre, O. Hellmuth, B. Bernhard and M. Cremer, “Audio id:towards content based identification of audio material,” 100th AES Convention, 2001.Google Scholar
  2. 2.
    S. Baluja and M. Covell, “Audio fingerprinting: Combining computer vision and data stream processing,” Proc. of ICME, 2007.Google Scholar
  3. 3.
    E. Batlle, J. Masip and E. Guaus, “Automatic song identification in noisy broadcast audio,” Proc. of SIP, Aug 2002.Google Scholar
  4. 4.
    M. Brand “Fast Low Rank Modifications of the Thin Singular Value Decomposition,” Linear Algebra and its Applications, pp. 20-30, 2006.Google Scholar
  5. 5.
    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.Google Scholar
  6. 6.
    E. Cohen et al., “Finding Interesting Associations without Support Pruning,” Knowledge and Data Engineering, Vol. 13, pp. 64–78, 2001.CrossRefGoogle Scholar
  7. 7.
    B. Coskun, B. Sankur, and N. Memon, “SpatioTemporal Transform Based Video Hashing,” IEEE Transactions on Multimedia, Vol. 8, no. 6, pp. 1190–1208, 2006.CrossRefGoogle Scholar
  8. 8.
    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.Google Scholar
  9. 9.
    J. Fridrich and M. Goljan, “Robust hash functions for digital watermarking,” ITCC, 2000.Google Scholar
  10. 10.
    J. Haitsma and T. Kalker, “A highly robust audio fingerprinting system,” Proc. of ISMIR, 2002.Google Scholar
  11. 11.
    M. Johnson and K. Ramchandran, “Dither-based Secure Image Hashing Using Distributed Coding,” Proc. of ICIP, 2003.Google Scholar
  12. 12.
    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.Google Scholar
  13. 13.
    Yan Ke, Derek Hoiem and Rahul Sukthanker, “Computer vision for music identification,” CVPR, 2005.Google Scholar
  14. 14.
    C. Kim, “Content-based image copy detection,” Signal Processing: Image Communication, vol. 18, pp. 169–184, 2003.CrossRefGoogle Scholar
  15. 15.
    S. Kim and C. D. Yoo, “Boosted binary audio fingerprint based on spectral subband moments,” ICASSP, 2007.Google Scholar
  16. 16.
    J. Kornblum “Identifying Almost Identical Files Using Context Triggered Piecewise Hashing,” Digital Investigation, 3(S):91–97, Proceedings of the Digital Forensic Workshop, 2006.Google Scholar
  17. 17.
    S.S. Kozat,R. Venkatesan, and M.K. Mihcak, “Robust perceptual image hashing via matrix invariants,” Proc. of ICIP, 2004.Google Scholar
  18. 18.
    F. Lefbvre, J. Czyz and B. Macq, “A robust soft hash algorithm for digital image signature,” Proceddings of European Signal Processing Conference, 2002.Google Scholar
  19. 19.
    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.MathSciNetCrossRefGoogle Scholar
  20. 20.
    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.MathSciNetCrossRefGoogle Scholar
  21. 21.
    D.G. Lowe, “Distinctive image features from scale invariant keypoints,” IEEE Journal on Computer Vision, 2004.Google Scholar
  22. 22.
    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.Google Scholar
  23. 23.
    S. Mavandadi, and P. Aarabi, “Rotation Invariance in Images,” Proc. of ICASSP, 2007.Google Scholar
  24. 24.
    M.K. Mihcak and R. Venkatesan, “New iterative geometric methods for robust perceptual image hashing,” Proc. of ICIP, 2000.Google Scholar
  25. 25.
    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.Google Scholar
  26. 26.
    M.K. Mihcak and R. Venkatesan, “A perceptual audio hashing algorithm: A tool for robust audio identification and information hiding,” ICIP, 2004.Google Scholar
  27. 27.
    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.Google Scholar
  28. 28.
    V. Monga and B. L. Evans, “Robust perceptual image hashing using feature points,” Proc. of ICIP, 2004.Google Scholar
  29. 29.
    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.Google Scholar
  30. 30.
    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.Google Scholar
  31. 31.
    H. Nicholas “DCFLDD Defense Computer Forensics Lab, http://dcfldd.sourceforge.net/” 2002.
  32. 32.
    J. Oostveen, T. Kalker and J. Haitsma, “Visual Hashing of Digital Video:Applications and Techniques,” Proc. of ACM Multimedia, New York, 2004.Google Scholar
  33. 33.
    H. Ozer, B. Sankur and N.D. Memon, “Robust audio hashing for audio identification,” Proc. of ICME, 2007.Google Scholar
  34. 34.
    R. Radhakrishnan, C. Bauer, C. Cheng and K. Terry, “Audio signature extraction based on projections of spectrograms,” Proc. of ICME, 2007.Google Scholar
  35. 35.
    R. Radhakrishnan, C. Bauer “Robust Video Fingerprints based on Subspace Embedding,” Proc. of ICASSP, 2008.Google Scholar
  36. 36.
    R. Radhakrishnan and C. Bauer, “Content-based video signatures based on projections of difference images,” IEEE Proc. on MMSP, 2007.Google Scholar
  37. 37.
    M. Schneider and S.F. Chang, “A robust content-based digital signature for image authentication,” IEEE Proc. on ICIP, 1996.Google Scholar
  38. 38.
    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.CrossRefGoogle Scholar
  39. 39.
    A. Swaminathan, Y. Mao, and M. Wu, “Image hashing resilient to geometric and filtering operations,” Proc. of MMSP, 2004.Google Scholar
  40. 40.
    A. Tridgell “Spamsum README from: http://samba.org/ftp/unpacked/junkcode/spamsum/readme” 2002.
  41. 41.
    R. Venkatesan, S.-M. Koon, M.H. Jakubowski and P. Moulin, “Robust image hashing,” Proc. of ICIP, 2000.Google Scholar
  42. 42.
    A. Wang, “Shazam,” CVPR, 2005.Google Scholar
  43. 43.
    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.Google Scholar
  44. 44.
    S.-H. Yang, and C.-F. Chen, “Robust Image Hashing based on SPIHT,” Proc. of ITRE, 2005.Google Scholar
  45. 45.
    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.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Dolby Laboratories IncSan Francisco

Personalised recommendations