Efficient Video Copy Detection Using Simple and Effective Extraction of Color Features

  • R. Roopalakshmi
  • G. Ram Mohana Reddy
Part of the Communications in Computer and Information Science book series (CCIS, volume 193)

Abstract

In the present Multimedia era, the exponential growth of illegal videos and huge piracy issues increased the importance of Content Based video Copy Detection (CBCD) techniques. CBCD systems require compact and computationally efficient descriptors for detecting video copies. In this paper, we propose a simple and efficient video signature scheme using Dominant Color Descriptors of MPEG-7 standard in order to implement the proposed CBCD task. Experimental results show that the proposed approach yields better detection rates when compared to that of existing approaches, against common transformations like Contrast change, Noise addition, Rotation, Zooming, Blurring etc. Further, evaluation results also prove that our scheme is computationally efficient by supporting substantial reduction in the total computational cost up to the extent of 65% when compared to that of existing schemes.

Keywords

Content-Based Video Copy Detection MPEG -7 Dominant Color Descriptor 

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References

  1. 1.
    Wu, X., Hgo, C.-W., Hauptmann, A.G., Tan, H.-K.: Real Time Near Duplicate Elimination for Web Video Search with Content and Context. IEEE Transactions on Multimedia 11(2) (2009)Google Scholar
  2. 2.
    Bhat, D., Nayar, S.: Ordinal Measures for Image Correspondence. IEEE Transactions on Pattern Analysis and Machine Intelligence, 415–423 (1998)Google Scholar
  3. 3.
    Shen, H.T., Zhou, X., Huang, Z., Shao, J.: UQLIPS: A real-time near-duplicate video clip detection system. In: VLDB (2007)Google Scholar
  4. 4.
    Lowe, D.G.: Distinctive Image Features from Scale-Invariant Key Points. Journal of Computer Vision, 91–110 (2004)Google Scholar
  5. 5.
    Bay, H., Tuytelaars, T, Van Gool, L.: SURF: Speeded Up Robust Features. Computer Vision and Image Understanding, 346–359 (2008)Google Scholar
  6. 6.
    Ke, Y., Sukthankar, R.: PCASIFT: A More Distinctive Representation for Local Image Descriptors. In: Computer Vision and Pattern Recognition (CVPR), pp. 506–513 (2004)Google Scholar
  7. 7.
    Manjunath, B.S., Salembier, P., Sikora, T.: Introduction to MPEG-7 - Multimedia Content Description Interface. John Wiley and Sons, West Sussex (2002)Google Scholar
  8. 8.
    Lloyd, S.P.: Least Squares Quantization in PCM. IEEE Transactions. Information Theory 28, 129–137 (1982)MathSciNetCrossRefMATHGoogle Scholar
  9. 9.
    Kashiwagi, T., Oe, S.: Introduction of Frequency Image and applications. In: SICE Annual Conference 2007, Japan (2007)Google Scholar
  10. 10.
    Deng, Y., Manjunath, B.S., Kenney, C., Moore, M.S., Shin, H.: An efficient color representation for image retrieval. IEEE Transactions on Image Processing 10, 140–147 (2001)CrossRefMATHGoogle Scholar
  11. 11.
    Roytman, E., Gotsman, C.: Dynamic Color Quantization of Video Sequences. IEEE Transactions on Visualization and Computer Graphics 1(3) (1995)Google Scholar
  12. 12.
    Open Video Project, http://www.open-video.org
  13. 13.
    Cho, H.-J., Lee, Y.-S., Sohn, C.-B., Chung, K.-S., Oh, S.-J.: A Novel Video Copy Detection Method Based on Statistical Analysis. In: International Conference on Multimedia & Expo. (2009)Google Scholar
  14. 14.
    Kim, J., Nam, J.: Content-based Video Copy Detection using Spatio-Temporal Compact Feature. In: International Conference on Advanced Communication Technology ICACT 2009 (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • R. Roopalakshmi
    • 1
  • G. Ram Mohana Reddy
    • 1
  1. 1.Information Technology DepartmentNational Institute of Technology Karnataka(NITK)MangaloreIndia

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