Multimedia Tools and Applications

, Volume 47, Issue 2, pp 279–306 | Cite as

Scaling content-based video copy detection to very large databases

  • Sébastien Poullot
  • Olivier Buisson
  • Michel Crucianu
Article

Abstract

Video copy detection is mainly required for protecting owners against unauthorized use of their content. Content-based copy detection methods rely on the evaluation of the similarity between potential copies and the original videos. Scalability is the key issue in making these methods practical for very large video databases. To address this challenge, we put forward here an optimized similarity-based search method that takes into account the local characteristics of the space of content signatures. First, refined models of the distortions undergone by the signatures during the copy creation process allow to search in a more appropriately defined area of the description space, increasing query selectivity and improving detection quality. Second, by identifying in the description space those regions where the local density of content signatures is high, a significant additional reduction of the computation cost is obtained. An evaluation on ground truth databases shows that the proposed solution is reliable. Scalability is then demonstrated on larger databases of up to 280,000 h of video.

Keywords

Content-based video copy detection Video retrieval Scalability Multidimensional index structure Video indexing 

References

  1. 1.
    Bay H, Tuytelaars T, Gool LJV (2006) SURF: speeded up robust features. In: Leonardis A, Bischof H, Pinz A (eds) Proc. European conf. on computer vision (ECCV’06), LNCS, vol 3951. Springer, New York, pp 404–417Google Scholar
  2. 2.
    Berrani S-A, Amsaleg L, Gros P (2003) Robust content-based image searches for copyright protection. In: Proc. 1st ACM intl. workshop on multimedia databases (MMDB’03), New Orleans, USA. ACM, New York, pp 70–77Google Scholar
  3. 3.
    Chang E, Wang J, Li C, Wilderhold G (1998) Rime—a replicated image detector for the world-wide web. In: Proc. SPIE symp. on voice, video and data comm., pp 58–67Google Scholar
  4. 4.
    Chum O, Philbin J, Isard M, Zisserman A (2007) Scalable near identical image and shot detection. In: Proc. 6th ACM intl. conf. on image and video retrieval (CIVR’07), Amsterdam, The Netherlands. ACM, New York, pp 549–556Google Scholar
  5. 5.
    Eickeler S, Muller S (1999) Content-based video indexing of TV broadcast news using hidden Markov models. In: Proc. IEEE int. conf. on acoustics, speech, and signal processing (ICASSP’99), Washington, DC, USA. IEEE Computer Society, Los Alamitos, pp 2997–3000Google Scholar
  6. 6.
    Fischler MA, Bolles RC (1981) Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun ACM 24(6):381–395CrossRefMathSciNetGoogle Scholar
  7. 7.
    Foo JJ (2007) Detection of near-duplicates in large image collections. Ph.D. diss., School of Comp. Sci. and Inf. Tech., Royal Melbourne Institute of Technology, Melbourne, VictoriaGoogle Scholar
  8. 8.
    Foo JJ, Zobel J, Sinha R, Tahaghoghi SMM (2007) Detection of near-duplicate images for web search. In: Proc. 6th ACM intl. conf. on image and video retrieval (CIVR’07), New York, NY, USA. ACM, New York, pp 557–564Google Scholar
  9. 9.
    Gengembre N, Berrani S-A (2008) A probabilistic framework for fusing frame-based searches within a video copy detection system. In: Proc. of ACM international conference on content-based image and video retrieval (CIVR), Niagara Falls, Canada. ACM, New York, pp 211–220CrossRefGoogle Scholar
  10. 10.
    Hampapur A, Hyun K, Bolle RM (2002) Comparison of sequence matching techniques for video copy detection. In: Yeung MM, Li C-S, Lienhart RW (eds) Proc. conf. on storage and retrieval for media databases, pp 194–201Google Scholar
  11. 11.
    Henrich A (1998) The LSDh-tree: an access structure for feature vectors. In: Proc. 14th intl. conf. on data engineering (ICDE’98), Washington, DC, USA. IEEE Computer Society, Los Alamitos, pp 362–369CrossRefGoogle Scholar
  12. 12.
    Jaimes A, Chang S-F, Loui AC (2003) Detection of non-identical duplicate consumer photographs. In: 4th Pacific-Rim conf. on multimedia, vol 1, pp 16–20Google Scholar
  13. 13.
    Joly A, Buisson O, Frélicot C (2007) Content-based copy detection using distortion-based probabilistic similarity search. IEEE Trans Multimedia 9(2):293–306CrossRefGoogle Scholar
  14. 14.
    Joly A, Frélicot C, Buisson O (2003) Robust content-based video copy identification in a large reference database. In: Intl. conf. on image and video retrieval (CIVR’03), pp 414–424Google Scholar
  15. 15.
    Joly A, Frélicot C, Buisson O (2005) Discriminant local features selection using efficient density estimation in a large database. In: Proc. 7th ACM SIGMM intl. workshop on multimedia information retrieval (MIR’05), New York, NY, USA. ACM, New York, pp 201–208CrossRefGoogle Scholar
  16. 16.
    Ke Y, Sukthankar R (2004) PCA-SIFT: a more distinctive representation for local image descriptors. In: IEEE conf. on comp. vision and pattern recognition (CVPR’04), vol 2, Los Alamitos, CA, USA. IEEE Computer Society, Los Alamitos, pp 506–513Google Scholar
  17. 17.
    Ke Y, Sukthankar R, Huston L (2004) An efficient parts-based near-duplicate and sub-image retrieval system. In: Proc. ACM intl. conf. on multimedia, pp 869–876Google Scholar
  18. 18.
    Kim C, Vasudev B (2005) Spatiotemporal sequence matching for efficient video copy detection. CirSysVideo 2005 15(1):127–132Google Scholar
  19. 19.
    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. 14th ACM intl. conf. on multimedia, New York, NY, USA. ACM, New York, pp 835–844CrossRefGoogle Scholar
  20. 20.
    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. 6th ACM intl. conf. on image and video retrieval (CIVR’07), New York, NY, USA. ACM, New York, pp 371–378Google Scholar
  21. 21.
    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/
  22. 22.
    Lin E, Eskicioglu A, Lagendijk R, Delp E (2005) Advances in digital video content protection. Proc IEEE 93(1):171–183CrossRefGoogle Scholar
  23. 23.
    Lowe DG (1999) Object recognition from local scale-invariant features. In: Proc. intl. conf. on computer vision (ICCV’99), vol 2, Washington, DC, USA. IEEE Computer Society, Los Alamitos, pp 1150–1157Google Scholar
  24. 24.
    Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110CrossRefGoogle Scholar
  25. 25.
    Marco B, Del Bimbo A, Nunziati W (2006) Video clip matching using MPEG-7 descriptors and edit distance. In: Proc. of ACM international conference on image and video retrieval (CIVR), LNCS, Tempe, AZ, pp 133–142Google Scholar
  26. 26.
    Mikolajczyk K, Schmid C (2001) Indexing based on scale invariant interest points. In: Proc. 8th intl. conf. on computer vision, pp 525–531Google Scholar
  27. 27.
    Mikolajczyk K, Schmid C (2004) Scale & affine invariant interest point detectors. Int J Comput Vis 60(1):63–86CrossRefGoogle Scholar
  28. 28.
    Mikolajczyk K, Tuytelaars T, Schmid C, Zisserman A, Matas J, Schaffalitzky F, Kadir T, Gool LV (2005) A comparison of affine region detectors. Int J Comput Vis 65(1–2):43–72CrossRefGoogle Scholar
  29. 29.
    Poullot S, Buisson O, Crucianu M (2007) Z-grid-based probabilistic retrieval for scaling up content-based copy detection. In: Proc. ACM intl. conf. on image and video retrieval (CIVR’07), Amsterdam, pp 348–355Google Scholar
  30. 30.
    Rothganger F, Lazebnik S, Schmid C, Ponce J (2006) 3D object modeling and recognition using local affine-invariant image descriptors and multi-view spatial constraints. Int J Comput Vis 66(3):231–259CrossRefGoogle Scholar
  31. 31.
    Samet H (2006) Foundations of multidimensional and metric data structures. Morgan Kaufmann, San FranciscoMATHGoogle Scholar
  32. 32.
    Schaffalitzky F, Zisserman A (2002) Multi-view matching for unordered image sets, or “how do I organize my holiday snaps?”. In: Proc. 7th European conf. on computer vision (ECCV’02), London, UK. Springer, New York, pp 414–431Google Scholar
  33. 33.
    Schmid C, Mohr R (1997) Local grayvalue invariants for image retrieval. IEEE Trans Pattern Anal Mach Intell 19(5):530–535CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Sébastien Poullot
    • 1
    • 2
  • Olivier Buisson
    • 2
  • Michel Crucianu
    • 1
  1. 1.Vertigo-CEDRICCNAMParis Cedex 03France
  2. 2.Institut National de l’AudiovisuelBry-sur-MarneFrance

Personalised recommendations