Advertisement

Near-Duplicate Video Retrieval Based on Spatiotemporal Pattern Tree

  • Ajay Kumar Mallick
  • Sushila Maheshkar
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 703)

Abstract

Recently, due to rapid advancement in multimedia devices and exponential increase in Internet user activities such as video editing, preview, and streaming accumulate enormous amount of near-duplicate videos which cannot be detected or retrieved effectively by conventional video retrieval technique. In this paper, we propose a simple but effective hierarchical spatiotemporal approach for high-quality near-duplicate video retrieval. Pattern generation of encoded key frames using angular distribution density is used which are translation and rotation invariant. Queue pool contributes temporal matching and consistency for the retrieval. Experimental result analysis demonstrates the effectiveness of the proposed method.

Keywords

Near-duplicate Angular density distribution Encoding Key frames Pattern Tree Queue pool Video retrieval CBVR 

References

  1. 1.
    Pickering M.J, and Ruger S., Evaluation of key frame-based retrieval techniques for video, Computer Vision and Image Understanding, Academic Press Inc Elsevier Science, pp. 217–235, 2003.Google Scholar
  2. 2.
    C. Kim and B. Vasudev, Spatiotemporal sequence matching for efficient video copy detection, IEEE Trans. Circuits Syst. Video Technol., vol. 15, no. 1, pp. 127–132, 2005.Google Scholar
  3. 3.
    C. Bohm, S. Berchitold, and D. A. Keim, Searching in highdimensional spaces: Index structures for improving the performance of multimedia databases, CM Comput. Survey, vol. 33, no. 3, pp. 322–373, 2001.Google Scholar
  4. 4.
    F. Hartung and M. Kutter, Multimedia watermarking techniques, Proc. IEEE, vol. 87, no. 7, pp. 1079–1107, 1999.Google Scholar
  5. 5.
    Yanqiang Lei, WeiqiLuo, Yuangen Wang, Jiwu Huang, Video Sequence Matching Based On The Invariance Of Color Correlation, IEEE Transactions On Circuits And Systems For Video Technology, vol. 22, no. 9, September 2012.Google Scholar
  6. 6.
    J. Liu, Z. Huang, H. Cai, H. T. Shen, C. W. Ngo, and W. Wang, Nearduplicate video retrieval: Current research and future trends, ACM Comput. Surveys, vol. 45, no. 4, pp. 1–23, 2013.Google Scholar
  7. 7.
    Rao, A., Srihari, R. K., AND Zhang, Z. Spatial color histograms for content-based image retrieval. Proceedings of the Eleventh IEEE International Conference on Tools with Artificial Intelligence, 1999.Google Scholar
  8. 8.
    X. Wu, C. W. Ngo, A. Hauptmann, and H. K. Tan, Real-time nearduplicate elimination for web video search with content and context, IEEE Trans. Multimedia, vol. 11, no. 2, pp. 196–207, 2009.Google Scholar
  9. 9.
    Z. Wu and K. Aizawa, Self-similarity-based partial near-duplicate video retrieval and alignment, Int. J. Multimedia Inf. Retrieval, vol. 3, no. 1, pp. 1–14, 2014.Google Scholar
  10. 10.
    C. Y. Chiu, C. S. Chen, and L. F. Chien, A framework for handling spatiotemporal variations in video copy detection, IEEE Trans. Circuits Syst. Video Technol., vol. 18, no. 3, pp. 412–417, 2008.Google Scholar
  11. 11.
    R. Roopalakshmi and G. R. M. Reddy, A novel spatio-temporal registration framework for video copy localization based on multimodal Features, Signal Process., vol. 93, no. 8, pp. 2339–2351, 2013.Google Scholar
  12. 12.
    C. L. Chou, H. T. Chen, Y. C. Chen, C. P. Ho, and S. Y. Lee, Near- duplicate video retrieval and localization using pattern set based dynamic programming, in Proc. 2013 IEEE Int. Conf. Multimedia Expo, pp. 1–6 Jul., 2013.Google Scholar
  13. 13.
    Y. Tian, T. Huang, M. Jiang, and W. Gao, Video copy-detection and localization with a scalable cascading framework, IEEE Multimedia, vol. 20, no. 3, pp. 72–86, Jul. Sep. 2013.Google Scholar
  14. 14.
    J. H. Su, Y. T. Huang, H. H. Yeh, and V. S. Tseng, Effective contentbased video retrieval using pattern indexing and matching techniques, Expert Syst. Appl., vol. 37, no. 7, pp. 5068–5085, 2010.Google Scholar
  15. 15.
    R. Chaudhry, A. Ravichandran, G. Hager, and R. Vidal, Histograms of oriented optical flow and Binet-Cauchy kernels on nonlinear dynamical systems for the recognition of human actions, in Proc. 2009 IEEE Conf. Comput. Vis. Pattern Recog., pp. 1932–1939, Jun. 2009.Google Scholar
  16. 16.
    E. Rosten, R. Porter, and T. Drummond, Faster and better: A machine learning approach to corner detection, IEEE Trans. Pattern Anal. Mach. Intell., vol. 32, no. 1, pp. 105–119, 2010.Google Scholar
  17. 17.
    Chou, C.L., Chen, H.T., Lee, S.Y.: Pattern-based near-duplicate video retrieval and localization on web-scale videos. IEEE Trans. Multimedia 17(3), 382–395, 2015.Google Scholar
  18. 18.
    The Open Video Project. (1998). A Shared Digital Video Collection [Online]. Available: http://www.open-video.org.

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringIndian Institute of Technology (Indian School of Mines)DhanbadIndia

Personalised recommendations