Exploring Information Theory and Gaussian Markov Random Fields for Color Texture Classification

  • Cédrick Bamba NsimbaEmail author
  • Alexandre L. M. Levada
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12132)


This paper proposes a novel approach to compute information theory measures from Gaussian Markov Random Field (GMRF) for color texture classification task. We firstly transform the three color channels of the input image into three set of sub-bands of the form LLHHHL and LH using three Discret Wavelet Transforms. We then visualize each sub-band as a GMRF from which we generate features by computing Fisher information matrix and Shannon’s entropy to encode the local spatial dependency. The concatenation of the computed features are then used as the texture descriptor, which in turn is used as input for the classifiers referred to in this work. Experiments were performed with color texture images from public databases widely used in the literature that demonstrate the efficiency of the proposed method.


Color texture classification Gaussian Markov random field Information theory Fisher information Texture descriptors 


  1. 1.
    Khan, F.S., van de Weijer, J., Vanrell, M.: Top-down color attention for object recognition. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 979–986 (2009)Google Scholar
  2. 2.
    van de Sande, K., Gevers, T., Snoek, C.: Evaluating color descriptors for object and scene recognition. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1582–1596 (2010)CrossRefGoogle Scholar
  3. 3.
    Nilsback, M., Zisserman, A.: Automated flower classification over a large number of classes. In: 2008 Sixth Indian Conference on Computer Vision, Graphics Image Processing, pp. 722–729, December 2008Google Scholar
  4. 4.
    Qi, X., Xiao, R., Li, C., Qiao, Y., Guo, J., Tang, X.: Pairwise rotation invariant co-occurrence local binary pattern. IEEE Trans. Pattern Anal. Mach. Intell. 36(11), 2199–2213 (2014)CrossRefGoogle Scholar
  5. 5.
    Pietikainen, M., Maenpaa, T., Viertola, J.: Color texture classification with color histograms and local binary patterns. In: Workshop on Texture Analysis in Machine Vision, January 2002Google Scholar
  6. 6.
    Li, W., Fritz, M.: Recognizing materials from virtual examples. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7575, pp. 345–358. Springer, Heidelberg (2012). Scholar
  7. 7.
    Sharan, L., Liu, C., Rosenholtz, R., Adelson, E.H.: Recognizing materials using perceptually inspired features. Int. J. Comput. Vision 103(3), 348–371 (2013)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Hayman, E., Caputo, B., Fritz, M., Eklundh, J.-O.: On the significance of real-world conditions for material classification. In: Pajdla, T., Matas, J. (eds.) ECCV 2004. LNCS, vol. 3024, pp. 253–266. Springer, Heidelberg (2004). Scholar
  9. 9.
    Kwitt, R., Meerwald, P.: Salzburg texture image database. Accessed Feb 2018
  10. 10.
    Jiang, L., Rich, W., Buhl-Brown, D.: Texture analysis of remote sensing imagery with clustering and Bayesian inference. Int. J. Image Graph. Sig. Proces. 7, 1–10 (2015)Google Scholar
  11. 11.
    Lerski, R.A., Straughan, K., Schad, L.R., Boyce, D.V.M., Bluml, S., Zuna, I.: MR image texture analysis-an approach to tissue characterization. Magn. Reson. Imaging 11(6), 873–87 (1993)CrossRefGoogle Scholar
  12. 12.
    Westerink, P.H., Biemond, J., Boekee, D.E.: Sub-band Image Coding, Kluwer Academic (1991). chapter Sub-band coding of color imagesGoogle Scholar
  13. 13.
    Mallat, S.G.: A theory of multiresolution image decomposition: the wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell. 11(7), 647–693 (1989)CrossRefGoogle Scholar
  14. 14.
    Do, M.N., Vetterli, M.: Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance. IEEE Trans. Image Process. 11(2), 146–158 (2002). Scholar
  15. 15.
    Allili, M.S.: Wavelet modeling using finite mixtures of generalized Gaussian distributions: application to texture discrimination and retrieval. IEEE Trans. Image Process. 21(4), 1452–1464 (2012). Scholar
  16. 16.
    Li, S.Z.: Markov Random Field Modeling in Image Analysis. Springer, Berlin (2001). Scholar
  17. 17.
    Petrou, M., Sevilla, P.G.: Image Processing. Texture: Dealing with Texture, 1st edn. Wiley John and Sons, West Sussex (2006)CrossRefGoogle Scholar
  18. 18.
    Van de Wouwer, G., Scheunders, P., Dyck, D.: Statistical texture characterization from discrete wavelet representation. IEEE Trans. Image Process. 8, 592–598 (1999). Scholar
  19. 19.
    Vetterli, M., Kovacevic, J.: Wavelets and Subband Coding. Prentice-Hall, Englewood Cliffs (1995)zbMATHGoogle Scholar
  20. 20.
    Raju, U.S.N., Vijaya Kumar, V., et al.: Texture classification based on extraction of skeleton primitives using wavelets. Inf. Technol. J. 7(6), 883–889 (2008)CrossRefGoogle Scholar
  21. 21.
    Ong, S., Jin, X., Jayasooriah, Sinniah, R.: Image analysis of tissue sections. Comput. Biol. Med. 26(3), 269–279 (1996). Information Retrieval and GenomicsGoogle Scholar
  22. 22.
    Liu, L., Chen, J., Fieguth, P., Zhao, G., Chellappa, R., Pietikainen, M.: From bow to CNN: two decades of texture representation for texture classification. Int. J. Comput. Vision 127(1), 74–109 (2019)CrossRefGoogle Scholar
  23. 23.
    Pietikainen, M., Hadid, A., Zhao, G., Ahonen, T.: Computer Vision Using Local Binary Patterns. Computational Imaging and Vision. Springer, London (2011). Scholar
  24. 24.
    Hammersley, J.M., Clifford, P.: Markov field on finite graphs and lattices, preprint (1971).
  25. 25.
    Haralick, R., Shanmugam, K., Dinstein, I.: Texture features for image classification. IEEE Trans. Syst. Man Cybern. 3, 610–621 (1973)CrossRefGoogle Scholar
  26. 26.
    Haralick, R.M.: Statistical and structural approaches to texture. Proc. IEEE 67, 786–804 (1979). Scholar
  27. 27.
    Dalal, N., Triggs, B.:. Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 1, pp. 886–893 (2005).
  28. 28.
    Qi, X., Qiao, Y., Li, C.-G., Guo, J.: Exploring Cross-Channel Texture Correlation for Color Texture Classification (2013).
  29. 29.
    Fujieda, S., Takayama, K., Hachisuka, T.: Wavelet convolutional neural networks for texture classification. arXive-prints, arXiv:1707.07394, July 2017
  30. 30.
    Hafemann, L.G., Oliveira, L.S., Cavalin, P.: Forest species recognition using deep convolutional neural net-works. In: 2014 22nd International Conference on Pattern Recognition, pp. 1103–1107, August 2014Google Scholar
  31. 31.
    Cimpoi, M., Maji, S., Kokkinos, I., Vedaldi, A.: Deep filter banks for texture recognition, description, and segmentation. Int. J. Comput. Vision 118(1), 65–94 (2016)MathSciNetCrossRefGoogle Scholar
  32. 32.
    Besag, J.: Spatial interaction and the statistical analysis of lattice systems. J. Roy. Stat. Soc. Ser. B. 36, 192–236 (1974)MathSciNetzbMATHGoogle Scholar
  33. 33.
    Andrearczyk, V., Whelan, P.: Using filter banks in convolutional neural networks for texture classification. Pattern Recogn. Lett. 84, 63–69 (2016)CrossRefGoogle Scholar
  34. 34.
    Zhao, Y., Zhang, L., Li, P., Huang, B.: Classification of high spatial resolution imagery using improved Gaussian Markov random-field-based texture features. IEEE Trans. Geosci. Remote Sens. 45(5), 1458–1468 (2007)CrossRefGoogle Scholar
  35. 35.
    Shannon, C., Weaver, W.: The Mathematical Theory of Communication. University of Illinois Press, Urbana (1949)zbMATHGoogle Scholar
  36. 36.
    Frieden, B.R.: Science from Fisher Information: A Unification. Cambridge University Press, Cambridge (2004)CrossRefGoogle Scholar
  37. 37.
    Frieden, B.R., Gatenby, R.A.: Exploratory Data Analysis Using Fisher Information. Springer, London (2006). CrossRefzbMATHGoogle Scholar
  38. 38.
    Hafner, G.M., Liedlgruber, A., Uhl, M., Vécsei, A., Wrba, F.: Combining Gaussian Markov random fields with the discrete-wavelet transform for endoscopic image classification. In: DSP 2009: 16th International Conference on Digital Signal Processing, Proceedings, pp. 1–6 (2009).
  39. 39.
    Mani, M.R., Subbaiah, K.V.: Texture Classification Method using Wavelet Transforms Based on Gaussian Markov Random Field (2010)Google Scholar
  40. 40.
    Porter, R., Canagarajah, N.: Robust rotation-invariant texture classification: wavelet, Gabor filter and GMRF based schemes. IEE Proc. Vision Image Sig. Process. 144(3), 180–188 (1997). Scholar
  41. 41.
    Levada, A.L.M.: learning from complex systems: on the roles of entropy and fisher information in pairwise isotropic Gaussian Markov random fields. Entropy, Special Issue Inf. Geometry. 16, 1002–1036 (2014)Google Scholar
  42. 42.
    Levada, A.L.M.: Information geometry, simulation and complexity in Gaussian random fields. Monte Carlo Methods Appl. 22(2), 81–107 (2016)MathSciNetCrossRefGoogle Scholar
  43. 43.
    Nsimba, C.B., Levada, A.L.M.: Nonlinear dimensionality reduction in texture classification: is manifold learning better than PCA? In: Rodrigues, J.M.F., et al. (eds.) ICCS 2019. LNCS, vol. 11540, pp. 191–206. Springer, Cham (2019). Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Cédrick Bamba Nsimba
    • 1
    Email author
  • Alexandre L. M. Levada
    • 1
  1. 1.Department of Computer ScienceFederal University of São CarlosSão CarlosBrazil

Personalised recommendations