Multimedia Tools and Applications

, Volume 62, Issue 1, pp 75–110 | Cite as

Competitive content-based video copy detection using global descriptors

Article

Abstract

Content-Based Video Copy Detection (CBVCD) consists of detecting whether or not a video document is a copy of some known original and to retrieve the original video. CBVCD systems rely on two different tasks: Feature Extraction task, that calculates many representative descriptors for a video sequence, and Similarity Search task, that is the algorithm for finding videos in an indexed collection that match a query video. This work details a CBVCD approach based on a combination of global descriptors, an automatic weighting algorithm, a pivot-based index structure, an approximate similarity search, and a voting algorithm for copy localization. This approach is analyzed using MUSCLE-VCD-2007 corpus, and it was tested at the TRECVID 2010 evaluation together with other state-of-the-art CBVCD systems. The results show that this approach enables global descriptors to achieve competitive results and even outperforms systems based on combination of local descriptors and audio information. This approach has a potential of achieving even higher effectiveness due to its seamless ability of combining descriptors from different sources at the similarity search level.

Keywords

Video copy detection Metric spaces Automatic weighting Approximate search Multimedia information retrieval 

References

  1. 1.
    Anguera X, Obrador P, Oliver N (2009) Multimodal video copy detection applied to social media. In: Proc. of the 1st SIGMM workshop on social media (WSM’09). ACM, pp 57–64Google Scholar
  2. 2.
    Barrios J, Bustos B (2010) Content-based video copy detection: PRISMA at TRECVID 2010. In: TRECVID. NISTGoogle Scholar
  3. 3.
    Barrios J, Bustos B (2011) P-VCD: a pivot-based approach for content-based video copy detection. In: Proc. of the IEEE int. conf. on multimedia and expo (ICME’11). IEEE, pp 1–6Google Scholar
  4. 4.
    Batko M, Kohoutkova P, Novak D (2009) Cophir image collection under the microscope. In: Proc. of the intl. workshop on similarity search and applications (SISAP’09). IEEE, pp 47–54Google Scholar
  5. 5.
    Bay H, Ess A, Tuytelaars T, Gool LV (2008) Speeded-up robust features (SURF). Comput Vis Image Underst 110(3):346–359CrossRefGoogle Scholar
  6. 6.
    Brin S (1995) Near neighbor search in large metric spaces. In: Proc. of the int. conf. on very large databases (VLDB’95). Morgan Kauffman, pp 574–584Google Scholar
  7. 7.
    Bustos B, Skopal T (2006) Dynamic similarity search in multi-metric spaces. In: Proc. of the int. workshop on multimedia information retrieval (MIR’06). ACM, pp 137–146Google Scholar
  8. 8.
    Bustos B, Pedreira O, Brisaboa N (2008) A dynamic pivot selection technique for similarity search. In: Proc. of the int. workshop on similarity search and applications (SISAP’08). IEEE, pp 105–112Google Scholar
  9. 9.
    Chávez E, Navarro G, Baeza-Yates R, Marroquín JL (2001) Searching in metric spaces. ACM Comput Surv 33(3):273–321CrossRefGoogle Scholar
  10. 10.
    Ciaccia P, Patella M, Zezula P (1997) M-tree: an efficient access method for similarity search in metric spaces. In: Proc. of the int. conf. on very large databases (VLDB’97). Morgan Kauffman, pp 426–435Google Scholar
  11. 11.
    Deselaers T, Weyand T, Ney H (2007) Image retrieval and annotation using maximum entropy. In: CLEF Workshop 2006. LNCS, vol 4730. Springer, pp 725–734Google Scholar
  12. 12.
    Douze M, Gaidon A, Jegou H, Marszalek M, Schmid C (2008) INRIA LEAR’s video copy detection system. In: TRECVID. NISTGoogle Scholar
  13. 13.
    Gupta V, Boulianne G, Cardinal P (2010) CRIM’s content-based audio copy detection system for TRECVID 2009. In: Proc. of the int. workshop on content-based multimedia indexing (CBMI’10). IEEEGoogle Scholar
  14. 14.
    Hampapur A, Bolle R (2001) Comparison of distance measures for video copy detection. In: Proc. of the IEEE int. conf. on multimedia and expo (ICME’01). IEEE, pp 737–740Google Scholar
  15. 15.
    Joly A, Buisson O, Frélicot C (2007) Content-based copy retrieval using distortion-based probabilistic similarity search. IEEE Trans Multimedia 9(2):293–306CrossRefGoogle Scholar
  16. 16.
    Kim C, Vasudev B (2005) Spatiotemporal sequence matching for efficient video copy detection. IEEE Trans Circuits Syst Video Technol 15(1):127–132CrossRefGoogle Scholar
  17. 17.
    Law-To J, Joly A, Boujemaa N (2007) MUSCLE-VCD-2007: a live benchmark for video copy detection. http://www-rocq.inria.fr/imedia/civr-bench/
  18. 18.
    Law-To J, Buisson O, Gouet-Brunet V, Boujemaa N (2006) Robust voting algorithm based on labels of behavior for video copy detection. In: Proc. of the int. conf. on multimedia (ACMMM’06), pp 835–844. ACMGoogle Scholar
  19. 19.
    Law-To J, Chen L, Joly A, Laptev I, Buisson O, Gouet-Brunet V, Boujemaa N, Stentiford F (2007) Video copy detection: a comparative study. In: Proc. of the int. conf. on image and video retrieval (CIVR’07). ACM, pp 371–378Google Scholar
  20. 20.
    Lew M, Sebe N, Djeraba C, Jain R (2006) Content-based multimedia information retrieval: state of the art and challenges. ACM Transactions on Multimedia Computing, Communications and Applications 2(1):1–19CrossRefGoogle Scholar
  21. 21.
    Lowe D (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110CrossRefGoogle Scholar
  22. 22.
    Manjunath BS, Ohm JR, Vasudevan VV, Yamada A (2001) Color and texture descriptors. IEEE Trans Circuits Syst Video Technol 11(6):703–715CrossRefGoogle Scholar
  23. 23.
    Natsev A, Smith JR, Hill M, Hua G, Huangy B, Merlery M, Xie L, Ouyangz H, Zhoux M (2010) IBM research TRECVID-2010 video copy detection and multimedia event detection system. In: TRECVID. NISTGoogle Scholar
  24. 24.
    Ngo CW, Zhu SA, Tan HK, Zhao WL, Wei XY (2010) VIREO at TRECVID 2010: semantic indexing, known-item search, and content-based copy detection. In: TRECVID. NISTGoogle Scholar
  25. 25.
    Poullot S, Buisson O, Crucianu M (2007) Z-grid-based probabilistic retrieval for scaling up content-based copy detection. In: Proc. of the int. conf. on image and video retrieval (CIVR’07). ACM, pp 348–355Google Scholar
  26. 26.
    Poullot S, Crucianu M, Buisson O (2008) Scalable mining of large video databases using copy detection. In: Proc. of the int. conf. on multimedia (ACMMM’08). ACM, pp 61–70Google Scholar
  27. 27.
    Sivic J, Zisserman A (2003) Video google: a text retrieval approach to object matching in videos. In: Proc. of the IEEE int. conf. on computer vision (ICCV’03), vol 2. IEEE, pp 1470–1477Google Scholar
  28. 28.
    Skopal T (2007) Unified framework for fast exact and approximate search in dissimilarity spaces. ACM Trans Database Syst 32(4):29–47CrossRefGoogle Scholar
  29. 29.
    Smeaton AF, Over P, Kraaij W (2006) Evaluation campaigns and TRECVid. In: Proc. of the int. workshop on multimedia information retrieval (MIR’06). ACM, pp 321–330Google Scholar
  30. 30.
    Smeulders AWM, Worring M, Santini S, Gupta A, Jain R (2000) Content-based image retrieval at the end of the early years. IEEE Trans Pattern Anal Mach Intell 22(12):1349–1380CrossRefGoogle Scholar
  31. 31.
    Younessian E, Anguera X, Adamek T, Oliver N, Marimon D (2010) Telefonica research at TRECVID 2010 content-based copy detection. In: TRECVID. NISTGoogle Scholar
  32. 32.
    Zezula P, Amato G, Dohnal V, Batko M (2005) Similarity search: the metric space approach (advances in database systems). SpringerGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.PRISMA Research Group, Department of Computer ScienceUniversity of ChileSantiagoChile

Personalised recommendations