Video copy detection by conducting fast searching of inverted files | Multimedia Tools and Applications Skip to main content
Springer Nature Link
Log in

Video copy detection by conducting fast searching of inverted files

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

Abstract

Fast content-based video copy detection is challenging because video databases have become extremely large. Conventional video-fingerprint-based copy detection systems that use the inverted-file approach involve many similarity computations based on the Hamming distance. To overcome this problem, a novel fast searching strategy for inverted files is proposed in this paper. The strategy involves simple table look-up and word counting operations for the fingerprint matching process. The similarity of video fragments is based on the number of matched fingerprints among all video candidates. In this method, the offset time is used, and fingerprints are ordered to further select the matched fingerprints from the video candidates. Moreover, a novel regional average fingerprint that is compatible with the proposed fast searching strategy is proposed. An experimental video copy detection system was used with the proposed algorithms, and the proposed algorithms were compared with other state-of-the-art fingerprinting algorithms on TRECVID 2011 dataset for different types of video distortions. In addition, VCDB dataset was also used to demonstrate the accuracy and efficiency of the proposed fast searching strategy while using inverted files to demonstrate the practicality of the method for a large database. The proposed system achieved higher accuracy on VCDB dataset with considerably higher operation speed compared with conventional inverted-file-based searching methods.

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
¥17,985 /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 includes VAT (Japan)

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

Similar content being viewed by others

References

  1. Abdel-Mottaleb M, Vaithilingam G, Krishnamachari S (1999) Signature-based image identification. In: Multimedia systems and applications II, international society for optics and photonics, vol 3845. pp 22-29

  2. Ayari M, Delhumeau J, Douze M, Jégou H, Potapov D, Revaud J, Schmid C, Yuan J (2011) Inria@ trecvid’2011: Copy detection & multimedia event detection. In: TRECVID

  3. Barrios JM, Bustos B (2013) Competitive content-based video copy detection using global descriptors. Multimedia Tools and Applications 62(1):75–110

    Article  Google Scholar 

  4. Chen L, Stentiford F (2008) Video sequence matching based on temporal ordinal measurement. Pattern Recogn Lett 29(13):1824–1831

    Article  Google Scholar 

  5. Cherubini M, De Oliveira R, Oliver N (2009) Understanding near-duplicate videos: a user-centric approach. In: Proceedings of the 17th ACM international conference on Multimedia, ACM, pp 35–44

  6. Coskun B, Sankur B, Memon N (2006) Spatio–temporal transform based video hashing. IEEE Transactions on Multimedia 8(6):1190–1208

    Article  Google Scholar 

  7. Devi S, Vishwanath N, Pillai SMP (2012) A robust video copy detection system using tiri-dct and dwt fingerprints. Int J Comput Appl 51(6):29–34

    Google Scholar 

  8. Douze M, Jégou H, Schmid C (2010) An image-based approach to video copy detection with spatio-temporal post-filtering. IEEE Trans Multimedia 12(4):257–266

    Article  Google Scholar 

  9. Esmaeili MM, Ward RK (2010) Robust video hashing based on temporally informative representative images. In: 2010 digest of technical papers international conference on consumer electronics (ICCE), IEEE, pp 179–180

  10. Esmaeili MM, Fatourechi M, Ward RK (2011) A robust and fast video copy detection system using content-based fingerprinting. IEEE Trans Inf Forensics Secur 6(1):213–226

    Article  Google Scholar 

  11. Esmaeili MM, Ward RK, Fatourechi M (2012) A fast approximate nearest neighbor search algorithm in the hamming space. IEEE Trans Pattern Anal Mach Intell 34(12):2481–2488

    Article  Google Scholar 

  12. Guzman-Zavaleta ZJ, Feregrino-Uribe C, Morales-Sandoval M, Menendez-Ortiz A (2017) A robust and low-cost video fingerprint extraction method for copy detection. Multimedia Tools and Applications 76(22):24143–24163

    Article  Google Scholar 

  13. Haitsma JA, Kalker AACM, Baggen CPMJ, Oostveen JC (2009) Generating and matching hashes of multimedia content. US Patent 7,549,052

  14. Hao Y, Mu T, Goulermas JY, Jiang J, Hong R, Wang M (2017) Unsupervised t-distributed video hashing and its deep hashing extension. IEEE Trans Image Process 26(11):5531–5544

    Article  MathSciNet  Google Scholar 

  15. Harvey RC, Hefeeda M (2012) Spatio-temporal video copy detection. In: Proceedings of the 3rd multimedia systems conference, ACM, pp 35–46

  16. Hong R, Yang Y, Wang M, Hua XS (2015) Learning visual semantic relationships for efficient visual retrieval. IEEE Transactions on Big Data 1(4):152–161

    Article  Google Scholar 

  17. Jegou H, Douze M, Schmid C (2008) Hamming embedding and weak geometric consistency for large scale image search. In: European conference on computer vision, Springer, pp 304–317

  18. Jiang YG, Wang J (2016) Partial copy detection in videos: a benchmark and an evaluation of popular methods. IEEE Transactions on Big Data 2(1):32–42

    Article  Google Scholar 

  19. Jiang YG, Jiang Y, Wang J (2014) Vcdb: A large-scale database for partial copy detection in videos. In: European Conference on Computer Vision, Springer, pp 357–371

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

    Article  Google Scholar 

  21. Jun W, Lee Y, Jun BM (2016) Duplicate video detection for large-scale multimedia. Multimedia Tools and Applications 75(23):15665–15678

    Article  Google Scholar 

  22. 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 

  23. Kordopatis-Zilos G, Papadopoulos S, Patras I, Kompatsiaris Y (2017) Near-duplicate video retrieval by aggregating intermediate cnn layers. In: International conference on multimedia modeling, Springer, pp 251–263

  24. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105

  25. Liao K, Liu G (2015) An efficient content based video copy detection using the sample based hierarchical adaptive k-means clustering. J Intell Inf Syst 44(1):133–158

    Article  Google Scholar 

  26. Liu J, Huang Z, Cai H, Shen HT, Ngo CW, Wang W (2013) Near-duplicate video retrieval: Current research and future trends. ACM Comput Surv (CSUR) 45(4):44

    Article  Google Scholar 

  27. Liu X, Sun J, Liu J (2013) Shot-based temporally respective frame generation algorithm for video hashing. In: 2013 IEEE international workshop on information forensics and security (WIFS), IEEE, pp 109–114

  28. Liu X, Zhao R, Li F, Liao S, Ding Y, Zou B (2017) Novel robust zero-watermarking scheme for digital rights management of 3d videos. Signal Process Image Commun 54:140–151

    Article  Google Scholar 

  29. Lu J (2009) Video fingerprinting for copy identification: from research to industry applications. In: Media forensics and security, international society for optics and photonics, vol 7254, p 725402

  30. Mao J, Xiao G, Sheng W, Hu Y, Qu Z (2016) A method for video authenticity based on the fingerprint of scene frame. Neurocomputing 173:2022–2032

    Article  Google Scholar 

  31. Oostveen J, Kalker T, Haitsma J (2002) Feature extraction and a database strategy for video fingerprinting. In: International conference on advances in visual information systems, Springer, pp 117–128

  32. Özkan S, Esen E, Akar GB (2014) Visual group binary signature for video copy detection. In: 2014 22nd international conference on pattern recognition (ICPR), IEEE, pp 3945–3950

  33. Poullot S, Crucianu M, Buisson O (2008) Scalable mining of large video databases using copy detection. In: Proceedings of the 16th ACM international conference on Multimedia, ACM, pp 61–70

  34. Radhakrishnan R, Bauer C (2007) Content-based video signatures based on projections of difference images. In: 2007. MMSP 2007. IEEE 9th Workshop on Multimedia signal processing, IEEE, pp 341–344

  35. Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M et al (2015) Imagenet large scale visual recognition challenge. Int J Comput Vis 115(3):211–252

    Article  MathSciNet  Google Scholar 

  36. Sarkar A, Singh V, Ghosh P, Manjunath BS, Singh A (2010) Efficient and robust detection of duplicate videos in a large database. IEEE Trans Circuits Syst Video Technol 20(6):870–885

    Article  Google Scholar 

  37. Shen HT, Zhou X, Huang Z, Shao J, Zhou X (2007) Uqlips: a real-time near-duplicate video clip detection system. In: Proceedings of the 33rd international conference on Very large data bases, VLDB Endowment, pp 1374–1377

  38. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv:14091556

  39. Sivic J, Zisserman A et al (2003) Video google: a text retrieval approach to object matching in videos. In: Iccv, vol 2, pp 1470–1477

  40. Smeulders AW, 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 

  41. Tan HK, Ngo CW, Hong R, Chua TS (2009) Scalable detection of partial near-duplicate videos by visual-temporal consistency. In: Proceedings of the 17th ACM international conference on Multimedia, ACM, pp 145–154

  42. Wang L, Bao Y, Li H, Fan X, Luo Z (2017) Compact cnn based video representation for efficient video copy detection. In: International conference on multimedia modeling, Springer, pp 576–587

  43. Wu X, Hauptmann AG, Ngo CW (2007) Practical elimination of near-duplicates from web video search. In: Proceedings of the 15th ACM international conference on Multimedia, ACM, pp 218–227

  44. Yang B, Gu F, Niu X (2006) Block mean value based image perceptual hashing. In: 2006. IIH-MSP’06 international conference on intelligent information hiding and multimedia signal processing, IEEE, pp 167–172

  45. Yuan F, Po LM, Liu M, Xu X, Jian W, Wong K, Cheung KW (2016) Shearlet based video fingerprint for content-based copy detection. Journal of Signal and Information Processing 7(02):84

    Article  Google Scholar 

  46. Zhou Z, Wang Y, Wu QJ, Yang CN, Sun X (2017) Effective and efficient global context verification for image copy detection. IEEE Trans Inf Forensics Secur 12(1):48–63

    Article  Google Scholar 

Download references

Acknowledgements

The work described in this paper was substantially supported financially by the City University of Hong Kong, Kowloon, Hong Kong, under the Grant number: 7004609.

Author information

Authors and Affiliations

  1. Department of Electronic Engineering, City University of Hong Kong, Kowloon Tong, Hong Kong

    Mengyang Liu, Lai-Man Po & Yasar Abbas Ur Rehman

  2. Tencent Video, Tencent Holdings Limited, ShenZhen, China

    Xuyuan Xu

  3. MINIEYE, ShenZhen, China

    Yuming Li

  4. SenseTime Group Limited, Beijing, China

    Litong Feng

Authors
  1. Mengyang Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lai-Man Po
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yasar Abbas Ur Rehman
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xuyuan Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yuming Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Litong Feng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mengyang Liu.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, M., Po, LM., Ur Rehman, Y.A. et al. Video copy detection by conducting fast searching of inverted files. Multimed Tools Appl 78, 10601–10624 (2019). https://doi.org/10.1007/s11042-018-6639-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-018-6639-4

Keywords

Search

Navigation