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N-Gram Signature for Video Copy Detection

  • Paween Khoenkaw
  • Punpiti Piamsa-nga
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 265)

Abstract

Typically, video copy detection can be done by comparing signatures of new content with of known contents in database. However, this method requires high computation for both database generation and signature detection. In this paper, we proposed an efficient and fast video signature for video copy protection. The video features of a scene are extracted and then transformed to be a signature as a bit-wise string. All string signatures then are stored and manipulated by n-gram based text retrieval algorithm, which is proposed as a replacement with computation-intensive content similarity detection algorithm. The evaluation on the CC_WEB_VIDEO dataset shows that its accuracy is 85% where our baseline algorithms achieved only 75%; however, our algorithm is around 20 times as fast as the baseline.

Keywords

Similarity Measure Video Matching Video Search Video Database 

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References

  1. 1.
  2. 2.
    Mogul, J.C., Chan, Y.M., Kelly, T.: Design, implementation, and evaluation of duplicate transfer detection in HTTP. In: Proceedings of the 1st Conference on Symposium on Networked Systems Design and Implementation, vol. 1, p. 4. USENIX Association, San Francisco (2004)Google Scholar
  3. 3.
    Hampapur, A., Hyun, K.-H., Bolle, R.: Comparison of Sequence Matching Techniques for Video Copy Detection (2000)Google Scholar
  4. 4.
    Wu, V.K.Y., Polychronopoulos, C.: Efficient real-time similarity detection for video caching and streaming. In: 2012 19th IEEE International Conference on Image Processing (ICIP), pp. 2249–2252 (2012)Google Scholar
  5. 5.
    BaoFeng, L., HaiBin, C., Zheng, C.: An Efficient Method for Video Similarity Search with Video Signature. In: 2010 International Conference on Computational and Information Sciences (ICCIS), pp. 713–716 (2010)Google Scholar
  6. 6.
    Sánchez, J.M., Binefa, X., Vitriá, J., Radeva, P.: Local Color Analysis for Scene Break Detection Applied to TV Commercials Recognition. In: Huijsmans, D.P., Smeulders, A.W.M. (eds.) VISUAL 1999. LNCS, vol. 1614, pp. 237–244. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  7. 7.
    Lienhart, R., Kuhmunch, C., Effelsberg, W.: On the detection and recognition of television commercials. In: IEEE International Conference on Multimedia Computing and Systems 1997, pp. 509–516 (1997)Google Scholar
  8. 8.
    Xian-Sheng, H., Xian, C., Hong-Jiang, Z.: Robust video signature based on ordinal measure. In: 2004 International Conference on Image Processing, ICIP 2004, vol. 681, pp. 685–688 (2014)Google Scholar
  9. 9.
    Cao, Z., Zhu, M.: An efficient video similarity search strategy for video-on-demand systems. In: 2nd IEEE International Conference on Broadband Network & Multimedia Technology, IC-BNMT 2009, pp. 174–178 (2009)Google Scholar
  10. 10.
    Shang, L., Yang, L., Wang, F., Chan, K.-P., Hua, X.-S.: Real-time large scale near-duplicate web video retrieval. In: Proceedings of the International Conference on Multimedia, pp. 531–540. ACM, Firenze (2010)Google Scholar
  11. 11.
    Mohan, R.: Video sequence matching. In: Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, vol. 3696, pp. 3697–3700 (1998)Google Scholar
  12. 12.
    Shivakumar, N., Indyk, G.I.P.: Finding pirated video sequences on the internet (1999)Google Scholar
  13. 13.
    Xie, Q., Huang, Z., Shen, H.T., Zhou, X., Pang, C.: Quick identification of near-duplicate video sequences with cut signature. World Wide Web 15, 355–382 (2012)CrossRefGoogle Scholar
  14. 14.
    Ardizzone, E., La Cascia, M., Molinelli, D.: Motion and color-based video indexing and retrieval. In: Proceedings of the 13th International Conference on Pattern Recognition, 1996, vol. 133, pp. 135–139 (1996)Google Scholar
  15. 15.
    Flickner, M., Sawhney, H., Niblack, W., Ashley, J., Qian, H., Dom, B., Gorkani, M., Hafner, J., Lee, D., Petkovic, D., Steele, D., Yanker, P.: Query by image and video content: the QBIC system. Computer 28, 23–32 (1995)CrossRefGoogle Scholar
  16. 16.
    Dong, W., Wang, Z., Charikar, M., Li, K.: Efficiently matching sets of features with random histograms. In: Proceedings of the 16th ACM International Conference on Multimedia, pp. 179–188. ACM, Vancouver (2008)CrossRefGoogle Scholar
  17. 17.
    Wan-Lei, Z., Xiao, W., Chong-Wah, N.: On the Annotation of Web Videos by Efficient Near-Duplicate Search. IEEE Transactions on Multimedia 12, 448–461 (2010)CrossRefGoogle Scholar
  18. 18.
    Junfeng, J., Xiao-Ping, Z., Loui, A.C.: A new video similarity measure model based on video time density function and dynamic programming. In: 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1201–1204 (2011)Google Scholar
  19. 19.
    Wu, X., Hauptmann, A.G., Ngo, C.-W.: Practical elimination of near-duplicates from web video search. In: Proceedings of the 15th International Conference on Multimedia, pp. 218–227. ACM, Augsburg (2007)CrossRefGoogle Scholar
  20. 20.
    Dunning, T.: Statistical Identification of Language. Computing Research Laboratory. New Mexico State University (1994)Google Scholar
  21. 21.
    Khoo, C.S.G., Loh, T.E.: Using statistical and contextual information to identify two-and three-character words in Chinese text. J. Am. Soc. Inf. Sci. Technol. 53, 365–377 (2002)CrossRefGoogle Scholar
  22. 22.
    Tomović, A., Janičić, P., Kešelj, V.: n-Gram-based classification and unsupervised hierarchical clustering of genome sequences. Computer Methods and Programs in Biomedicine 81, 137–153 (2006)CrossRefGoogle Scholar
  23. 23.
    Pavlović-Lažetić, G.M., Mitić, N.S., Beljanski, M.V.: n-Gram characterization of genomic islands in bacterial genomes. Computer Methods and Programs in Biomedicine 93, 241–256 (2009)CrossRefGoogle Scholar
  24. 24.
    Radomski, J.P., Slonimski, P.P.: Primary sequences of proteins from complete genomes display a singular periodicity: Alignment-free N-gram analysis. Comptes Rendus Biologies 330, 33–48 (2007)CrossRefGoogle Scholar
  25. 25.
    Xiao, W., Chong-Wah, N., Hauptmann, A.G., Hung-Khoon, T.: Real-Time Near-Duplicate Elimination for Web Video Search With Content and Context. IEEE Transactions on Multimedia 11, 196–207 (2009)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Computer Engineering, Faculty of EngineeringKasetsart UniversityBangkokThailand

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