Skip to main content
SpringerLink
Log in

Competitive content-based video copy detection using global descriptors

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

Notes

  1. In particular, the system achieves recall 1 for ST1 and recall 90.5% for ST2. Despite EH10 descriptor is not invariant to mirroring, the system can detect a flipped copy from ST1 by just matching the mostly symmetrical frames.

  2. In fact, under the same conditions from Fig. 8 with segments of one second length, the detection performance for this example increases from recall 0.484 (15 detected copies) to 0.710 (22 detected copies) for precision 1.

References

  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 57–64

  2. Barrios J, Bustos B (2010) Content-based video copy detection: PRISMA at TRECVID 2010. In: TRECVID. NIST

  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 1–6

  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 47–54

  5. Bay H, Ess A, Tuytelaars T, Gool LV (2008) Speeded-up robust features (SURF). Comput Vis Image Underst 110(3):346–359

    Article  Google Scholar 

  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 574–584

  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 137–146

  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 105–112

  9. Chávez E, Navarro G, Baeza-Yates R, Marroquín JL (2001) Searching in metric spaces. ACM Comput Surv 33(3):273–321

    Article  Google Scholar 

  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 426–435

  11. Deselaers T, Weyand T, Ney H (2007) Image retrieval and annotation using maximum entropy. In: CLEF Workshop 2006. LNCS, vol 4730. Springer, pp 725–734

  12. Douze M, Gaidon A, Jegou H, Marszalek M, Schmid C (2008) INRIA LEAR’s video copy detection system. In: TRECVID. NIST

  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). IEEE

  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 737–740

  15. Joly A, Buisson O, Frélicot C (2007) Content-based copy retrieval using distortion-based probabilistic similarity search. IEEE Trans Multimedia 9(2):293–306

    Article  Google Scholar 

  16. Kim C, Vasudev B (2005) Spatiotemporal sequence matching for efficient video copy detection. IEEE Trans Circuits Syst Video Technol 15(1):127–132

    Article  Google Scholar 

  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. 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. ACM

  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 371–378

  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):1–19

    Article  Google Scholar 

  21. Lowe D (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110

    Article  Google Scholar 

  22. Manjunath BS, Ohm JR, Vasudevan VV, Yamada A (2001) Color and texture descriptors. IEEE Trans Circuits Syst Video Technol 11(6):703–715

    Article  Google Scholar 

  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. NIST

  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. NIST

  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 348–355

  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 61–70

  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 1470–1477

  28. Skopal T (2007) Unified framework for fast exact and approximate search in dissimilarity spaces. ACM Trans Database Syst 32(4):29–47

    Article  Google Scholar 

  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 321–330

  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):1349–1380

    Article  Google Scholar 

  31. Younessian E, Anguera X, Adamek T, Oliver N, Marimon D (2010) Telefonica research at TRECVID 2010 content-based copy detection. In: TRECVID. NIST

  32. Zezula P, Amato G, Dohnal V, Batko M (2005) Similarity search: the metric space approach (advances in database systems). Springer

Download references

Author information

Authors and Affiliations

  1. PRISMA Research Group, Department of Computer Science, University of Chile, Av. Blanco Encalada 2120 3er Piso, 8370459, Santiago, Chile

    Juan Manuel Barrios & Benjamin Bustos

Authors
  1. Juan Manuel Barrios
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Benjamin Bustos
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Juan Manuel Barrios.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Barrios, J.M., Bustos, B. Competitive content-based video copy detection using global descriptors. Multimed Tools Appl 62, 75–110 (2013). https://doi.org/10.1007/s11042-011-0915-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-011-0915-x

Keywords

Search

Navigation