Advertisement

Color Texture Analysis and Classification: An Agent Approach Based on Partially Self-avoiding Deterministic Walks

  • André Ricardo Backes
  • Alexandre Souto Martinez
  • Odemir Martinez Bruno
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6419)

Abstract

Recently, we have proposed a novel approach of texture analysis that has overcome most of the state-of-art methods. This method considers independent walkers, with a given memory, leaving from each pixel of an image. Each walker moves to one of its neighboring pixels according to the difference of intensity between these pixels, avoiding returning to recent visited pixels. Each generated trajectory, after a transient time, ends in a cycle of pixels (attractor) from where the walker cannot escape. The transient time (t) and cycle period (p) form a joint probability distribution, which contains image pixel organization characteristics. Here, we have generalized the texture based on the deterministic partially self avoiding walk to analyze and classify colored textures. The proposed method is confronted with other methods, and we show that it overcomes them in color texture classification.

Keywords

partially self-avoiding deterministic walks texture analysis color images 

References

  1. 1.
    Backes, A.R., Gonçalves, W.N., Martinez, A.S., Bruno, O.M.: Texture analysis and classification using deterministic tourist walk. Pattern Recognition 43, 685–694 (2009)CrossRefzbMATHGoogle Scholar
  2. 2.
    Tuceryan, M., Jain, A.K.: Texture analysis. In: Handbook of Pattern Recognition and Computer Vision, pp. 235–276 (1993)Google Scholar
  3. 3.
    Wu, C.M., Chen, Y.C., Hsieh, K.S.: Texture features for classification of ultrasonic liver images. IEEE Transactions on Medical Imaging 11, 141–152 (1992)CrossRefGoogle Scholar
  4. 4.
    Heidelbach, F., Kunze, K., Wenk, H.R.: Texture analysis of a recrystallized quartzite using electron diffraction in the scanning electron microscope. Journal of Structural Geology, 91–104 (2000)Google Scholar
  5. 5.
    Anguiano, E., Oliva, A.I., Aguilar, M.: Surface texture parameters as a tool to measure image quality in scanning probe microscope. Ultramicroscopy 77(3), 195–205 (1999)CrossRefGoogle Scholar
  6. 6.
    Zhang, J., Tan, T.: Brief review of invariant texture analysis methods. Pattern Recognition 35(3), 735–747 (2002), http://lear.inrialpes.fr/pubs/2002/ZT02 CrossRefzbMATHGoogle Scholar
  7. 7.
    Azencott, R., Wang, J.-P., Younes, L.: Texture classification using windowed Fourier filters. IEEE Trans. Pattern Anal. Mach. Intell. 19(2), 148–153 (1997)CrossRefGoogle Scholar
  8. 8.
    Jain, A.K., Farrokhnia, F.: Unsupervised texture segmentation using gabor filters. Pattern Recogn. 24(12), 1167–1186 (1991)CrossRefGoogle Scholar
  9. 9.
    Haralick, R.M.: Statistical and structural approaches to texture. Proc. IEEE 67(5), 768–804 (1979)CrossRefGoogle Scholar
  10. 10.
    Chetverikov, D.: Texture analysis using feature-based pairwise interaction maps. Pattern Recognition 32(3), 487–502 (1999)CrossRefGoogle Scholar
  11. 11.
    Chaudhuri, B.B., Sarkar, N.: Texture segmentation using fractal dimension. IEEE Trans. Pattern Anal. Mach. Intell. 17(1), 72–77 (1995)CrossRefGoogle Scholar
  12. 12.
    Backes, A.R., Bruno, O.M.: A new approach to estimate fractal dimension of texture images. In: Elmoataz, A., Lezoray, O., Nouboud, F., Mammass, D. (eds.) ICISP 2008. LNCS, vol. 5099, pp. 136–143. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  13. 13.
    Campiteli, M.G., Martinez, A.S., Bruno, O.M.: An image analysis methodology based on deterministic tourist walks. In: Sichman, J.S., Coelho, H., Rezende, S.O. (eds.) IBERAMIA 2006 and SBIA 2006. LNCS (LNAI), vol. 4140, pp. 159–167. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  14. 14.
    Backes, A.R., Bruno, O.M., Campiteli, M.G., Martinez, A.S.: Deterministic tourist walks as an image analysis methodology based. In: Martínez-Trinidad, J.F., Carrasco Ochoa, J.A., Kittler, J. (eds.) CIARP 2006. LNCS, vol. 4225, pp. 784–793. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  15. 15.
    Lima, G.F., Martinez, A.S., Kinouchi, O.: Deterministic walks in random media. Phys. Rev. Lett. 87(1), 010603 (2001)CrossRefGoogle Scholar
  16. 16.
    Stanley, H.E., Buldyrev, S.V.: Statistical physics - the salesman and the tourist. Nature (London) 413(6854), 373–374 (2001)CrossRefGoogle Scholar
  17. 17.
    Campiteli, M.G., Batista, P.D., Kinouchi, O., Martinez, A.S.: Deterministic walks as an algorithm of pattern recognition. Physical Review E (Statistical, Nonlinear, and Soft Matter Physics) 74(2), 026703 (2006)CrossRefGoogle Scholar
  18. 18.
    Martinez, A.S., Kinouchi, O., Risau-Gusman, S.: Exploratory behavior, trap models, and glass transitions. Phys. Rev. E 69(1), 017101 (2004)CrossRefGoogle Scholar
  19. 19.
    Terçariol, C.A., Martinez, A.S.: Analytical results for the statistical distribution related to a memoryless deterministic walk: dimensionality effect and mean-field models. Phys Rev. E 72Google Scholar
  20. 20.
    Tercariol, C.A.S., Martinez, A.S.: Influence of memory in deterministic walks in random media: Analytical calculation within a mean-field approximation. Phys. Rev. E 78(3), 031111 (2008)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Risau-Gusman, S., Martinez, A.S., Kinouchi, O.: Escaping from cycles through a glass transition. Phys. Rev. E 68, 016104 (2003)CrossRefGoogle Scholar
  22. 22.
    Paschos, G.: Fast color texture recognition using chromaticity moments. Pattern Recognition Letters 21(9), 837–841 (2000)CrossRefGoogle Scholar
  23. 23.
    She, A.C., Huang, T.S.: Segmentation of road scenes using color and fractal-based texture classification. In: ICIP, vol. (3), pp. 1026–1030 (1994)Google Scholar
  24. 24.
  25. 25.
    Everitt, B.S., Dunn, G.: Applied Multivariate Analysis, 2nd edn. Arnold, London (2001)zbMATHGoogle Scholar
  26. 26.
    Fukunaga, K.: Introduction to Statistical Pattern Recognition, 2nd edn. Academic Press, London (1990)zbMATHGoogle Scholar
  27. 27.
    Loncaric, S.: A survey of shape analysis techniques. Pattern Recognition 31(9), 983–1001 (1998)CrossRefGoogle Scholar
  28. 28.
    da Fontoura Costa, L., Cesar Jr., R.M.: Shape Analysis and Classification: Theory and Practice. CRC Press, Boca Raton (2000)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • André Ricardo Backes
    • 1
  • Alexandre Souto Martinez
    • 2
  • Odemir Martinez Bruno
    • 3
  1. 1.Faculdade de ComputaçãoUniversidade Federal de UberlândiaUberlândiaBrasil
  2. 2.Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto (FFCLRP)Universidade de São Paulo (USP)Ribeirão PretoBrazil
  3. 3.Instituto de Física de São Carlos (IFSC)Universidade de São Paulo (USP)São CarlosBrazil

Personalised recommendations