Skip to main content
Springer Nature Link
Log in

Content-based copy detection by a subspace learning based video fingerprinting scheme

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

Abstract

We propose a video copy detection scheme that employs a transform domain global video fingerprinting method. Video fingerprinting has been performed by the subspace learning based on nonnegative matrix factorization (NMF). It is shown that the binary video fingerprints extracted from the basis and gain matrices of the NMF representation enable us to efficiently represent the spatial and temporal content of a video segment respectively. An extensive performance evaluation has been carried out on the query and reference dataset of CBCD task of TRECVID 2011. Our results are compared with the average and the best performance reported for the task. Also NDCR and F1 rates are reported in comparison to the performance achieved via the global methods designed by the TRECVID 2011 participants. Results demonstrate that the proposed method achieves higher correct detection rates with good localization capability for the transformation of text/logo insertion, strong re-encoding, frame dropping, noise addition, gamma change or their mixtures; however there is still potential for improvement to detect copies with picture-in-picture transformations. It is also concluded that the introduced binary fingerprinting scheme is superior to the existing transform based methods in terms of the compactness.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

References

  1. Anguera X, Adamek T, Xu D, Barrios JM (2011) Telefonica research at TRECVID 2011 content-based copy detection. TRECVID Workshop: NIST

  2. Barrios JM, Bustos B, Anguera X (2011) Combining features at search time: PRISMA at video copy detection task. TRECVID Workshop: NIST

  3. Bay H, Ess A, Tuytelaars T, Gool LV (2008) SURF: Speeded Up Robust Features. Comp Vision Image Underst (CVIU) 110(3):346–359

    Article  Google Scholar 

  4. Bucak SS, Gunsel B (2007) Video content representation by incremental non-negative matrix factorization. Proc Int Conf Image Process ICIP 2:113–116

    Google Scholar 

  5. Cirakman O, Gunsel B, Sengör NS, Gursoy O (2010) Key-frame based video fingerprinting by NMF. Proceedings of International Conference on Image Processing ICIP, pp 2373–2376

  6. Fridrich J, Goljan M (2000) Robust hash functions for digital watermarking. Proceedings of International Conference on Information Technology: Coding and Computing ITCC, pp 178–183

  7. Gupta V, Varcheie PDZ, Gagnon L, Boulianne G (2011) CRIM at TRECVID 2011: content-based copy detection using nearest-neighbor mapping. TRECVID Workshop: NIST

  8. Gursoy O, Gunsel B, Sengor NS (2009) Transform invariant video fingerprinting by NMF. Proceedings of 13th International Conference on Computer Analysis of Images and Patterns CAIP, pp 452–459

  9. Hill M et al (2010) IBM research TRECVID-2010 video copy detection and multimedia event detection system. TRECVID Workshop: NIST 1:146–154

  10. Hradiš M, Řezníček I, Behúň K, Otrusina L (2011) Brno University of Technology at TRECVid 2011 SIN, CCD. TRECVID Workshop: NIST

  11. Jégou H, Douze M, Gravier G, Schmid C, Gros P (2010) INRIA LEAR-TEXMEX: video copy detection task. TRECVID Workshop: NIST 1:160–169

  12. Jiang M, Fang S, Tian Y, Huang T, Gao W (2011) PKU-IDM @ TRECVid 2011 CBCD: content-based copy detection with cascade of multimodal features and temporal pyramid matching. TRECVID Workshop: NIST

  13. Küçüktunç O, Baştan M, Güdükbay U, Ulusoy O (2010) Video copy detection using multiple visual cues and MPEG-7 descriptors. J Vis Commun Image Represent JVCI 21(8):838–849. doi:10.1016/j.jvcir.2010.07.001

    Article  Google Scholar 

  14. Law-To J, Chen L, Joly A, Laptev I, Buisson O (2007) Video copy detection: a comparative study. Proceedings of sixth ACM International Conference on Image and Video Retrieval CIVR, pp. 371–378

  15. Lee D, Seung H (2001) Algorithms for non-negative matrix factorization. Proc Adv Neural Inf Process Syst NIPS 13:556–562

    Google Scholar 

  16. Lee S, Yoo CD (2006) Video fingerprinting based on centroids of gradient orientations. Proceedings of International Conference on Acoustics, Speech and Signal Processing ICASSP, Toulouse, France 2:401–404

  17. Lee S, Yoo CD (2008) Robust video fingerprinting based on 2D-OPCA of affine covariant regions. Proceedings of the 2nd International Conference on Image Processing ICIP, San Diego, USA, pp 2156–2159

  18. Lin Y et al (2010) Nanjing University at TRECVid 2010: content-based copy detection task. TRECVID Workshop: NIST 1:322–328

  19. Liu Z, Zavesky E, Zhou N, Shahraray B (2011) AT&T Research at TRECVID 2011. TRECVID Workshop: NIST

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

    Article  Google Scholar 

  21. Massoudi A, Lefebvre F, Demarty CH, Oisel L, Chupeau B (2006) A video fingerprint based on visual digest and local fingerprints. Proceedings of International Conference on Image Processing ICIP, Atlanta, pp 2297–2300

    Google Scholar 

  22. Monga V, Mıhçak MK (2007) Robust and secure image hashing via non-negative matrix factorizations. IEEE Trans Inf Forensics Secur 2(3–1):376–390

    Article  Google Scholar 

  23. Mukai R, Kurozumi T, Kawanishi T, Nagano H, Kashino K (2011) NTT communication science laboratories at TRECVID 2011 content-based copy detection. TRECVID Workshop: NIST

  24. Naturel X, Gros P (2005) A fast shot matching strategy for detecting duplicate sequences in a television stream. Proceedings of the Second International Workshop on Computer Vision meets Databases CVDB, Baltimore, MD, pp 21–27

  25. Oostveen J, Kalker T, Haitsma J (2002) Feature extraction and a database strategy for video fingerprinting. Proceedings of International Conference on Visual Information and Information Systems VISUAL, pp 117–128

  26. Over P, George A, Fiscus J, Antonishek B, Michel M (2011) TRECVID 2011-goals, tasks, data, evaluation mechanisms and metrics. Proceedings of the TRECVID 2011 Workshop, NIST Gaithersburg, MD, USA, pp 1–56

  27. Radhakrishnan R, Jiang W, Bauer C (2009) A review of video fingerprints invariant to geometric attacks. Proc SPIE 7254:725407. doi:10.1117/12.805627

    Article  Google Scholar 

  28. Rouhi AH, Thom JA (2011) RMIT University at TRECVID 2011 content-based copy detection. TRECVID Workshop: NIST

  29. Sarkar A, Singh V, Ghosh P, Manjunath BS, Singh A (2010) Efficient and robust detection of duplicate videos in a large database. IEEE Trans Circ Syst Video Technol. doi:10.1109/TCSVT.2010.2046056

  30. Smeaton AF, Over P, Kraaij W (2006) Evaluation campaigns and TRECVid. In Proc. of the 8th ACM Int. Workshop on Multimedia Information Retrieval, Santa Barbara, CA, USA, MIR ‘06. ACM Press, New York, NY, 321–330. doi: http://doi.acm.org/10.1145/1178677/1178722

  31. Sun C, Li J, Zhang B, Zhang Q (2010) THU-IMG at TRECVID 2010. TRECVID Workshop: NIST 1:404–409

  32. Uchida Y, Takagi K, Sakazawa S (2011) KDDI Labs at TRECVID 2011: content-based copy detection. TRECVID Workshop: NIST

  33. Zhao W, Borth D, Breuel TM (2011) Participation at TRECVID 2011 semantic ındexing & content-based copy detection tasks. TRECVID Workshop: NIST

Download references

Acknowledgments

This work is supported by The Scientific and Technological Research Council of Turkey (TÜBİTAK) under the project name TUBITAK EEEAG PNo 109E63.

Author information

Authors and Affiliations

  1. Multimedia Signal Processing and Pattern Recognition Lab., Department of Electronics and Communication Engineering, Istanbul Technical University, 34469 Maslak, Istanbul, Turkey

    Ozgun Cirakman, Bilge Gunsel, Neslihan Serap Sengor & Sezer Kutluk

Authors
  1. Ozgun Cirakman
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Bilge Gunsel
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Neslihan Serap Sengor
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Sezer Kutluk
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Ozgun Cirakman.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Cirakman, O., Gunsel, B., Sengor, N.S. et al. Content-based copy detection by a subspace learning based video fingerprinting scheme. Multimed Tools Appl 71, 1381–1409 (2014). https://doi.org/10.1007/s11042-012-1269-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-012-1269-8

Keywords