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Mathematical Problems in Engineering
Volume 2013, Article ID 762472, 7 pages
Research Article

Wavelet-Based Dynamic Texture Classification Using Gumbel Distribution

1College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
2College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150001, China

Received 14 December 2012; Revised 31 March 2013; Accepted 8 April 2013

Academic Editor: Jun Jiang

Copyright © 2013 Yu-Long Qiao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. D. Chetverikov and R. P. Péteri, “A brief survey of dynamic texture description and recognition,” in Proceedinga of the International Conference on Computer Recognition Systems (CORES '05), pp. 17–26, 2005.
  2. S. Dubois, R. Peteri, and M. Menard, “Decomposition of dynamic textures using morphological component analysis,” IEEE Transactions Circuits and Systems for Video Technology, vol. 22, no. 2, pp. 188–201, 2012. View at Publisher · View at Google Scholar
  3. G. Doretto, A. Chiuso, Y. N. Wu, and S. Soatto, “Dynamic textures,” International Journal of Computer Vision, vol. 51, no. 2, pp. 91–109, 2003. View at Publisher · View at Google Scholar
  4. A. B. Chan, Beyond dynamic textures: a family of stochastic dynamical models for video with applications to computer vision [Ph.D. thesis], University of California San Diego, 2008.
  5. S. K. Choy and C. S. Tong, “Statistical wavelet subband characterization based on generalized gamma density and its application in texture retrieval,” IEEE Transactions on Image Processing, vol. 19, no. 2, pp. 281–289, 2010. View at Publisher · View at Google Scholar · View at MathSciNet
  6. Y. L. Qiao, C. Y. Song, and J. W. Qi, “Wavelet based dynamic texture classification using Weibull distribution,” ICIC Express Letters, vol. 5, no. 11, pp. 4053–4058, 2011. View at Google Scholar
  7. I. W. Selesnick, R. G. Baraniuk, and N. G. Kingsbury, “The dual-tree complex wavelet transform,” IEEE Signal Processing, vol. 22, no. 6, pp. 123–151, 2005. View at Publisher · View at Google Scholar
  8. T. Celik and T. Tjahjadi, “Multiscale texture classification using dual-tree complex wavelet transform,” Pattern Recognition Letters, vol. 30, no. 3, pp. 331–339, 2009. View at Publisher · View at Google Scholar
  9. Y. L. Qiao, C. H. Zhao, and C. Y. Song, “Complex wavelet based texture classification,” Neurocomputing, vol. 72, no. 16–18, pp. 3957–3963, 2009. View at Publisher · View at Google Scholar
  10. E. Castillo, A. S. Hadi, N. Balakrishnan, and J. M. Sarabia, Extreme Value and Related Models with Applications in Engineering and Science, Wiley Series in Probability and Statistics, John Wiley & Sons, Hoboken, NJ, USA, 2005. View at MathSciNet
  11. H. Rinne, The Weibull Distribution, CRC Press, Boca Raton, Fla, USA, 2009. View at MathSciNet
  12. G. Manuel, On Renyi divergence measures for continuous alphabet sources [M.S. thesis], Department of Mathematics and Statistics, Queen's University, Ontario, Canada, 2011.
  13. C. C. Chang and C. J. Lin, “LIBSVM: a library for support vector machines,” ACM Transactions on Intelligent Systems and Technology, vol. 2, no. 3, 2011, View at Google Scholar
  14. R. P. Péteri, S. Fazekas, and M. J. Huiskes, “DynTex: a comprehensive database of dynamic textures,” Pattern Recognition Letters, vol. 31, no. 12, pp. 1627–1632, 2010. View at Google Scholar