Skip to main content
SpringerLink
Log in
SpringerLink
Log in

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

  • Conference paper
  • First Online:
Image Analysis and Recognition (ICIAR 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12132))

Included in the following conference series:

  • 885 Accesses

Abstract

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 LL, HH, HL 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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  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. 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)

    Article  Google Scholar 

  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 2008

    Google Scholar 

  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)

    Article  Google Scholar 

  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 2002

    Google Scholar 

  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). https://doi.org/10.1007/978-3-642-33765-9_25

    Chapter  Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  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). https://doi.org/10.1007/978-3-540-24673-2_21

    Chapter  Google Scholar 

  9. Kwitt, R., Meerwald, P.: Salzburg texture image database. http://www.wavelab.at/sources/STex/. Accessed Feb 2018

  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. 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)

    Article  Google Scholar 

  12. Westerink, P.H., Biemond, J., Boekee, D.E.: Sub-band Image Coding, Kluwer Academic (1991). chapter Sub-band coding of color images

    Google Scholar 

  13. Mallat, S.G.: A theory of multiresolution image decomposition: the wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell. 11(7), 647–693 (1989)

    Article  Google Scholar 

  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). https://doi.org/10.1109/83.982822

    Article  MathSciNet  Google Scholar 

  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). https://doi.org/10.1109/TIP.2011.2170701

    Article  MathSciNet  MATH  Google Scholar 

  16. Li, S.Z.: Markov Random Field Modeling in Image Analysis. Springer, Berlin (2001). https://doi.org/10.1007/978-4-431-67044-5

    Book  MATH  Google Scholar 

  17. Petrou, M., Sevilla, P.G.: Image Processing. Texture: Dealing with Texture, 1st edn. Wiley John and Sons, West Sussex (2006)

    Book  Google Scholar 

  18. Van de Wouwer, G., Scheunders, P., Dyck, D.: Statistical texture characterization from discrete wavelet representation. IEEE Trans. Image Process. 8, 592–598 (1999). https://doi.org/10.1109/83.753747

    Article  Google Scholar 

  19. Vetterli, M., Kovacevic, J.: Wavelets and Subband Coding. Prentice-Hall, Englewood Cliffs (1995)

    MATH  Google Scholar 

  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)

    Article  Google Scholar 

  21. Ong, S., Jin, X., Jayasooriah, Sinniah, R.: Image analysis of tissue sections. Comput. Biol. Med. 26(3), 269–279 (1996). Information Retrieval and Genomics

    Google Scholar 

  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)

    Article  Google Scholar 

  23. Pietikainen, M., Hadid, A., Zhao, G., Ahonen, T.: Computer Vision Using Local Binary Patterns. Computational Imaging and Vision. Springer, London (2011). https://doi.org/10.1007/978-0-85729-748-8. https://books.google.com.br/books?id=wBrZz9FiERsC

    Book  Google Scholar 

  24. Hammersley, J.M., Clifford, P.: Markov field on finite graphs and lattices, preprint (1971). www.statslab.cam.ac.uk/grg/books/hammfest/hamm-cliff.pdf

  25. Haralick, R., Shanmugam, K., Dinstein, I.: Texture features for image classification. IEEE Trans. Syst. Man Cybern. 3, 610–621 (1973)

    Article  Google Scholar 

  26. Haralick, R.M.: Statistical and structural approaches to texture. Proc. IEEE 67, 786–804 (1979). https://doi.org/10.1109/proc.1979.11328

    Article  Google Scholar 

  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). http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1467360

  28. Qi, X., Qiao, Y., Li, C.-G., Guo, J.: Exploring Cross-Channel Texture Correlation for Color Texture Classification (2013). https://doi.org/10.5244/C.27.97

  29. Fujieda, S., Takayama, K., Hachisuka, T.: Wavelet convolutional neural networks for texture classification. arXive-prints, arXiv:1707.07394, July 2017

  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 2014

    Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  32. Besag, J.: Spatial interaction and the statistical analysis of lattice systems. J. Roy. Stat. Soc. Ser. B. 36, 192–236 (1974)

    MathSciNet  MATH  Google Scholar 

  33. Andrearczyk, V., Whelan, P.: Using filter banks in convolutional neural networks for texture classification. Pattern Recogn. Lett. 84, 63–69 (2016)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  35. Shannon, C., Weaver, W.: The Mathematical Theory of Communication. University of Illinois Press, Urbana (1949)

    MATH  Google Scholar 

  36. Frieden, B.R.: Science from Fisher Information: A Unification. Cambridge University Press, Cambridge (2004)

    Book  Google Scholar 

  37. Frieden, B.R., Gatenby, R.A.: Exploratory Data Analysis Using Fisher Information. Springer, London (2006). https://doi.org/10.1007/978-1-84628-777-0

    Book  MATH  Google Scholar 

  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). https://doi.org/10.1109/ICDSP.2009.5201226

  39. Mani, M.R., Subbaiah, K.V.: Texture Classification Method using Wavelet Transforms Based on Gaussian Markov Random Field (2010)

    Google Scholar 

  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). https://doi.org/10.1049/ip-vis:19971182

    Article  Google Scholar 

  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. Levada, A.L.M.: Information geometry, simulation and complexity in Gaussian random fields. Monte Carlo Methods Appl. 22(2), 81–107 (2016)

    Article  MathSciNet  Google Scholar 

  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). https://doi.org/10.1007/978-3-030-22750-0_15

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Federal University of São Carlos, São Carlos, SP, Brazil

    Cédrick Bamba Nsimba & Alexandre L. M. Levada

Authors
  1. Cédrick Bamba Nsimba
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alexandre L. M. Levada
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Cédrick Bamba Nsimba .

Editor information

Editors and Affiliations

  1. University of Porto, Porto, Portugal

    Aurélio Campilho

  2. University of Waterloo, Waterloo, ON, Canada

    Fakhri Karray

  3. University of Waterloo, Waterloo, ON, Canada

    Zhou Wang

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Nsimba, C.B., Levada, A.L.M. (2020). Exploring Information Theory and Gaussian Markov Random Fields for Color Texture Classification. In: Campilho, A., Karray, F., Wang, Z. (eds) Image Analysis and Recognition. ICIAR 2020. Lecture Notes in Computer Science(), vol 12132. Springer, Cham. https://doi.org/10.1007/978-3-030-50516-5_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-50516-5_12

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-50515-8

  • Online ISBN: 978-3-030-50516-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation