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Pixel-Based Object Detection and Tracking with Ensemble of Support Vector Machines and Extended Structural Tensor

  • Bogusław Cyganek
  • Michał Woźniak
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7653)

Abstract

In this paper we propose a system for visual object detection and tracking based on the extended structural tensor and the ensemble of one-class support vector machines. First, the input color image is transformed with the anisotropic process into the extended structural tensor. Then the tensor space is clustered into the number of partitions which are used to train a corresponding number of one-class support vector machines composing an ensemble of classifiers. In run-time the ensemble classifies the input video stream into an object and background. Thanks to high discriminative properties of the extended structural tensor and to the diversity of the ensemble of classifiers the method shows very good properties which were shown by experiments on real video sequences.

Keywords

Support Vector Machine Object Detection Anisotropic Diffusion Structural Tensor Tensor Space 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Arsigny, V., Fillard, P., Pennec, X., Ayache, N.: Log-Euclidean Metrics for Fast and Simple Calculus on Diffusion Tensors. Magnetic Resonance in Medicine 56(2), 411–421 (2006)CrossRefGoogle Scholar
  2. 2.
    Brox, T., Rousson, M., Derich, R., Weickert, J.: Unsupervised Segmentation Incorporating Colour, Texture, and Motion. INRIA Technical Report No 4760 (2003)Google Scholar
  3. 3.
    Chang, C.-C., Lin, C.-J.: LIBSVM, a library for support vector machines (2001), http://www.csie.ntu.edu.tw/~cjlin/libsvm
  4. 4.
    Comaniciu, D., Meer, P.: Mean Shift: A Robust Approach Toward Feature Space Analysis. IEEE Transactions on Pattern Analysis And Machine Intelligence 24(5), 603–619 (2002)CrossRefGoogle Scholar
  5. 5.
    Cyganek, B., Siebert, J.P.: An Introduction to 3D Computer Vision Techniques and Algorithms. Wiley (2009)Google Scholar
  6. 6.
    Cyganek, B.: Framework for Object Tracking with Support Vector Machines, Structural Tensor and the Mean Shift Method. In: Leung, C.S., Lee, M., Chan, J.H. (eds.) ICONIP 2009, Part I. LNCS, vol. 5863, pp. 399–408. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  7. 7.
    Cyganek, B.: One-Class Support Vector Ensembles for Image Segmentation and Classification. J. of Math. Imaging & Vision 42(2-3), 103–117 (2012)MathSciNetCrossRefGoogle Scholar
  8. 8.
  9. 9.
    Duda, Hart, Stork: Pattern Classification. Wiley (2001)Google Scholar
  10. 10.
    Forsyth, D.A., Ponce, J.: Computer Vision. A Modern Approach. Prentice-Hall (2003)Google Scholar
  11. 11.
    Jähne, B.: Digital Image Processing. Springer (2005)Google Scholar
  12. 12.
    Kuncheva, L.: Combining Pattern Classifiers. Methods and Algorithms. Wiley (2004)Google Scholar
  13. 13.
    Lee, H.-C.: Introduction to Color Imaging Science. Cambridge University Press (2005)Google Scholar
  14. 14.
    de Luis-García, R., Deriche, R., Rousson, M., Alberola-López, C.: Tensor Processing for Texture and Colour Segmentation. In: Kalviainen, H., Parkkinen, J., Kaarna, A. (eds.) SCIA 2005. LNCS, vol. 3540, pp. 1117–1127. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  15. 15.
    Moon, T.K., Stirling, W.C.: Mathematical Methods and Algorithms for Signal Processing. Prentice-Hall (2000)Google Scholar
  16. 16.
    Peeters, T., Rodrigues, P., Vilanova, A., ter Haar Romeny, B.: Analysis of distance/similarity measures for diffusion tensor imaging. In: Visualization and Processing of Tensor Fields: Advances and Perspectives, pp. 113–136. Springer, Berlin (2008)Google Scholar
  17. 17.
    Pennec, X., Fillard, P., Ayache, N.: A Riemannian framework for tensor computing. International Journal of Computer Vision 66(1), 41–66 (2006)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Perona, P., Malik, J.: Scale-Space and Edge Detection Using Anisotropic Diffusion. IEEE Trans. on Pattern Analysis and Machine Intelligence 12(7), 629–639 (1990)CrossRefGoogle Scholar
  19. 19.
    Press, W.H., Teukolsky, S.A., Vetterling, W.T., Flannery, B.P.: Numerical Recipes in C. The Art of Scientific Computing, 2nd edn. Cambridge University Press (1999)Google Scholar
  20. 20.
    Rittner, L., Flores, F.C., Lotufo, R.A.: A tensorial framework for color images. Pattern Recognition Letters 31(4), 277–296 (2010)CrossRefGoogle Scholar
  21. 21.
    Sapiro, G.: Geometric Partial Differential Equations and Image Analysis. Cambridge (2001)Google Scholar
  22. 22.
    Schölkopf, B., Smola, A.J.: Learning with Kernels. MIT Press (2002)Google Scholar
  23. 23.
    Shawe-Taylor, J., Cristianini, N.: Kernel Methods for Pattern Analysis. Cambridge University Press (2004)Google Scholar
  24. 24.
    Tax, D.M.J., Duin, R.P.W.: Support Vector Data Description. Machine Learning 54, 45–66 (2004)zbMATHCrossRefGoogle Scholar
  25. 25.
    Wang, Z., Vemuri, B.C.: DTI segmentation using an information theoretic tensor dissimilarity measure. IEEE Transactions on Medical Imaging 24(10), 1267–1277 (2005)CrossRefGoogle Scholar
  26. 26.
    Zabih, R., Woodfill, J.: Non-parametric Local Transforms for Computing Visual Correspondence. Computer Science Department, Cornell University, Ithaca (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Bogusław Cyganek
    • 1
  • Michał Woźniak
    • 2
  1. 1.AGH University of Science and TechnologyKrakówPoland
  2. 2.Wroclaw University of TechnologyWrocławPoland

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