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High Dimensional versus Low Dimensional Chaos in MPEG-7 Feature Binding for Object Classification

  • Hanif Azhar
  • Aishy Amer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6134)

Abstract

We proposed [1] a feature binding method to generate a MPEG-7 compliant feature vector, defined as C-MP7. Here, we study the excellence of C-MP7 as a feature vector, using either low- or high-dimensional chaos. With high-dimensional chaos-based C-MP7, we find, 1) the accuracy in SVM classifier improves 10% to 20%, for all classes of video objects over MPEG-7, 2) larger binary class separation among video objects in different classes, 3) vehicle objects are clustered well, which leads to above 99% accuracy for only vehicles against other objects in SVM, and 4) drifts in chaotic attractors allow the C-MP7 to include subtle variations in coefficients for video objects.

Keywords

Chaos MPEG-7 Video Object Feature 

References

  1. 1.
    Azhar, H., Amer, A.: Chaos and MPEG-7 Based Feature Vector for Video Object Classification. In: IEEE International Conference on Image Processing, pp. 432–437 (2008)Google Scholar
  2. 2.
    Ma, X., Eric, W., Grimson, L.: Edge-based rich representation for vehicle classification. In: IEEE International Conference on Computer Vision, pp. 1185–1192 (2005)Google Scholar
  3. 3.
    Qiming, L., Khoshgoftaar, T.M., Folleco, A.: Classification of Ships in Surveillance Video. In: IEEE International Conference on Information Reuse and Integration, September 2006, pp. 432–437 (2006)Google Scholar
  4. 4.
    Eidenberger, H.: How Good are the Visual MPEG-7 Features? In: SPIE Visual Communications and Image Processing, vol. 5150, pp. 476–488 (2003)Google Scholar
  5. 5.
    Basar, E. (ed.): Chaos in Brain Function. Springer, Heidelberg (1990)Google Scholar
  6. 6.
    Hubel, D.H.: Eye, Brain, and Vision, 2nd edn. W. H. Freeman, New York (1995)Google Scholar
  7. 7.
    Skarda, C.A., Freeman, W.J.: How Brains Make Chaos in Order to Make Sense of the World. Behav. Brain Sci. 10, 161–195 (1987)CrossRefGoogle Scholar
  8. 8.
    Kaplan, D., Glass, L.: Understanding Nonlinear Dynamics. Springer, Heidelberg (1998)Google Scholar
  9. 9.
    Takens, F.: Dynamical Systems and Turbulence. Lecture Notes in Mathematics, p. 898. Springer, Heidelberg (1981)Google Scholar
  10. 10.
    Grassberger, P., Procaccia, I.: Characterization of Strange Attractors. Phys. Rev. Lett. 50, 346–349 (1983)CrossRefMathSciNetGoogle Scholar
  11. 11.
    Amer, A.: Voting-based Simultaneous Tracking of Multiple Video Objects. IEEE Transactions on Circuits and Systems for Video Technology 15, 1448–1462 (2005)CrossRefGoogle Scholar
  12. 12.
    Manjunath, B.S., Salembier, P., Sikora, T.: Introduction to MPEG-7. John Wiley and Sons, Ltd., Chichester (2002)Google Scholar
  13. 13.
    Javed, O., Shah, M., Shafique, K.: Automated Visual Surveillance in Realistic Scenarios. IEEE Multimeda Magazine (2007)Google Scholar
  14. 14.
    Jack, L., Guo, H., Nandi, A.: Feature Generation Using Genetic Programming with Application to Fault Classification. IEEE Transactions on Systems, Man and Cybernetics Part B: Cybernetics 35(1) (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Hanif Azhar
    • 1
  • Aishy Amer
    • 1
  1. 1.Electrical and Computer EngineeringConcordia UniversityMontréalCanada

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