Regression Models for Texture Image Analysis

  • Anatoliy Plastinin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6744)


The article describes universal model for creating algorithms for calculating textural image features. The proposed models are used for images that are realizations of Markov Random Field. Experimental classification results are shown for different images sets.


Textutre Image Analysis Textutre Image Recognition Regression models Markov Random Fields 


  1. 1.
    Haralick, R.M.: Statistical and structural approaches to texture. Proceedings of the IEEE 67(5), 786–804 (1979)CrossRefGoogle Scholar
  2. 2.
    Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural Features for Image Classification. IEEE Transactions on Systems, Man, and Cybernetics SMC-3(6), 610–621 (1973)CrossRefGoogle Scholar
  3. 3.
    Tamura, H., Mori, S., Yamawaki, T.: Textural Features Corresponding to Visual Perception. IEEE Transaction on Systems, Man, and Cybernetcs SMC-8(6), 460–472 (1978)CrossRefGoogle Scholar
  4. 4.
    Winkler, G.: Image Analysis, Random Fields and Dynamic Monte Carlo Methods, p. 324. Springer, Heidelberg (1995)CrossRefzbMATHGoogle Scholar
  5. 5.
    Paget, R.: Nonparametric Markov Random Field Models for Natural Textures Images, Ph.D. thesis, University of Queensland, St Lucia, QLD Australia (December 1999)Google Scholar
  6. 6.
    Kaulgud, N., Desai, U.B.: Efficient color image restoration using Markov random field. In: TENCON 1998. 1998 IEEE Region 10 International Conference on Global Connectivity in Energy, Computer, Communication and Control, vol. 1, pp. 41–44 (1998)Google Scholar
  7. 7.
    Li, S.Z.: Markov Random Field Modeling in Image Analysis, 3rd edn. Springer, Heidelberg (2009)zbMATHGoogle Scholar
  8. 8.
    Scholkopf, B., Smola, A.J.: Learning with Kernels Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Cambridge (2001)Google Scholar
  9. 9.
    Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. Data Mining, Inference, and Prediction, 2nd edn. Springer, Heidelberg (2009)zbMATHGoogle Scholar
  10. 10.
    Engel, Y., Mannor, S., Meir, R.: The Kernel Recursive Least-Squares Algorithm. IEEE Transactions on Signal Processing 52(8) (2004)Google Scholar
  11. 11.
    Vapnik, V.: Statistical Learning Theory. Wiley, Chichester (1998)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Anatoliy Plastinin
    • 1
  1. 1.Samara State Aerospace UniversitySamaraRussia

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