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Multimedia Tools and Applications

, Volume 78, Issue 22, pp 31959–31986 | Cite as

An information-theoretic wavelet-based texture descriptor using Gaussian Markov random field models

  • Cédrick Bamba NsimbaEmail author
  • Alexandre Levada
Article
  • 61 Downloads

Abstract

Texture characterization and identification is a key issue for a variety of computer vision and image processing applications. Current techniques developed for dealing with the purpose thereof still present performance issues when applied in the presence of noise, owing to the intrinsic properties of the image being analyzed can not be maintained. Based on the principle that data distribution of these textures form a non-deterministic complex system, mathematical tools can help to characterize them. In this paper, we propose new approaches capable of quantifying such intrinsic properties by means of the Fisher information matrix. The methodology consists in firstly defining each wavelet sub-band of the texture image as a complex system modeled by a Gaussian Markov Random Field and secondly computing their respective Fisher information matrix and Shannon entropy. Applying the proposed texture descriptor to Salzburg and Outex datasets revealed a significant superiority of the proposed method vis-à-vis the majority of traditional and novel texture descriptors presented in the literature.

Keywords

Texture classification Gaussian markov random field Information theory Fisher information 

Notes

Acknowledgements

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001.

References

  1. 1.
    Arivazhagan S, Ganesan L (2003) Texture classification using wavelet transform. Pattern Recogn Lett 24:1513–1521.  https://doi.org/10.1016/S0167-8655(02)00390-2. http://www.sciencedirect.com/science/article/pii/0167865502003902 CrossRefzbMATHGoogle Scholar
  2. 2.
    Barrow D, Crone S (2016) Cross-validation aggregation for combining autoregressive neural network forecasts. Int J Forecast 32:11201137.  https://doi.org/10.1016/j.ijforecast.2015.12.011. The full text is currently unavailable on the repositoryGoogle Scholar
  3. 3.
    Besag J (1974) Spatial interaction and the statistical analysis of Lattice systems. J R Stat Soc Ser Spatial interaction B 36:192–236MathSciNetzbMATHGoogle Scholar
  4. 4.
    Chellappa R, Chatterjee S (1985) Classification of textures using gaussian Markov random fields. IEEE Trans Acoust Speech Signal Process 33:959–963.  https://doi.org/10.1109/TASSP.1985.1164641 MathSciNetCrossRefGoogle Scholar
  5. 5.
    Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20:273–297.  https://doi.org/10.1023/A:1022627411411 CrossRefzbMATHGoogle Scholar
  6. 6.
    Cross GR, Jain AK (1983) Markov random field texture models. IEEE Trans Pattern Anal Mach Intell PAMI-5:25–39.  https://doi.org/10.1109/TPAMI.1983.4767341 CrossRefGoogle Scholar
  7. 7.
    Cohen J (1960) A coefficient of agreement for nominal scales. Educ Psychol Meas 20(1):37–46CrossRefGoogle Scholar
  8. 8.
    Congalton RG (1991) A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environ 37(1):35–46CrossRefGoogle Scholar
  9. 9.
    Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: IEEE Computer society conference on computer vision and pattern recognition, 2005. CVPR 2005, vol 1, pp 886–893. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1467360
  10. 10.
    Dharmagunawardhana C, Mahmoodi S, Bennett M, Niranjan M (2014) Gaussian Markov random field based improved texture descriptor for image segmentation. Image Vis Comput 32:884–895.  https://doi.org/10.1016/j.imavis.2014.07.002. http://www.sciencedirect.com/science/article/pii/0262885614001127 CrossRefGoogle Scholar
  11. 11.
    Emerson WC (1998) Multi-scale fractal analysis of image texture and patternGoogle Scholar
  12. 12.
    Guo G, Wang H, Bell D, Bi Y, Greer K (2003) KNN model-based approach in classification. Springer, Berlin, pp 986–996Google Scholar
  13. 13.
    Hafemann LG, Oliveira LS, Cavalin P (2014) Forest species recognition using deep convolutional neural networks. In: 22nd International conference on pattern recognition (ICPR), pp 1103–1107, 2Google Scholar
  14. 14.
    Han J, Kamber M, Pei J (2006) Data mining: concepts and techniques. Seconded. Morgan Kaufmann Publishers, San FranciscozbMATHGoogle Scholar
  15. 15.
    Haralick RM (1979) Statistical and structural approaches to texture. Proc IEEE 67:786–804.  https://doi.org/10.1109/proc.1979.11328 CrossRefGoogle Scholar
  16. 16.
    Haralick R, Shanmugam K, Dinstein I (1973) Texture features for image classification. IEEE Trans Syst Man Cybern, 3Google Scholar
  17. 17.
    Hubel DH, Wiesel TN (1968) Receptive fields and functional architecture of monkey striate cortex. J Physiol 195.1:215–243CrossRefGoogle Scholar
  18. 18.
    Jensen A, Cour-Harbo A (2011) Ripples in mathematics: the discrete wavelet transform. Springer, Berlin. https://books.google.com.br/books?id=nMIPBwAAQBAJ zbMATHGoogle Scholar
  19. 19.
    Kaplan LM (1999) Extended fractal analysis for texture classification and segmentation. IEEE Trans Image Process 8:1572–1585.  https://doi.org/10.1109/83.799885 CrossRefGoogle Scholar
  20. 20.
    Kass RE (1989) The geometry of asymptotic inference. Statist Sci 4:188–219.  https://doi.org/10.1214/ss/1177012480 MathSciNetCrossRefzbMATHGoogle Scholar
  21. 21.
    Krishnamachari S, Chellappa R (1997) Multiresolution gauss-markov random field models for texture segmentation. IEEE Trans Image Process 6:251–267.  https://doi.org/10.1109/83.551696 CrossRefGoogle Scholar
  22. 22.
    Kwitt R, Meerwald P (2017) Salzburg texture image database, online Available: http://www.wavelab.at/sources/STex/
  23. 23.
    LeCun Y, Bengio Y, Hinton G (2015) Deep learning nature publishing group, a division of Macmillan Publishers Limited, 521.  https://doi.org/10.1038/nature14539
  24. 24.
    Levada A (2014) Learning from complex systems: on the roles of entropy and fisher information in pairwise isotropic gaussian Markov random fields. Entropy 16:1002.  https://doi.org/10.3390/e16021002. http://www.mdpi.com/1099-4300/16/2/1002de gruyterCrossRefGoogle Scholar
  25. 25.
    Levada AL (2016) Information geometry, simulation and complexity in gaussian random fields. de gruyter 22:81107.  https://doi.org/10.1515/mcma-2016-0107 MathSciNetCrossRefzbMATHGoogle Scholar
  26. 26.
    Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60:91–110.  https://doi.org/10.1023/B:VISI.0000029664.99615.94 CrossRefGoogle Scholar
  27. 27.
    Musgrave FK, Peachey D, Perlin K, Worley S (1994) Texturing and modeling: a procedural approach. Academic Press Professional, Inc., San DiegoGoogle Scholar
  28. 28.
    Ojala T, Maenpaa T, Pietikainen M, Viertola J, Kyllonen J, Huovinen S (2002) Outex – new framework for empirical evaluation of texture analysis algorithms. In: 16th international conference on pattern recognition, volume 1 of ICPR, pp 701–706Google Scholar
  29. 29.
    Pietikainen M, Hadid A, Zhao G, Ahonen T (2011) Computer vision using local binary patterns. Computational imaging and vision. Springer, London. https://books.google.com.br/books?id=wBrZz9FiERsC CrossRefGoogle Scholar
  30. 30.
    Sa Junior JJDM, Backes AR, Cortez PC (2013) Texture analysis and classification using shortest paths in graphs. Pattern Recogn Lett 34:1314–1319.  https://doi.org/10.1016/j.patrec.2013.04.013 CrossRefGoogle Scholar
  31. 31.
    Yang C, Zhang L, Lu H, Ruan X, Yang M (2013) Saliency detection via graph-based manifold ranking. In: Proc. IEEE int. conf. computer vision and pattern recognition. IEEE, pp 3166–3173Google Scholar
  32. 32.
    Santafe G, Inza I, Jose AL (2015) Dealing with the evaluation of supervised classification algorithms. Artif Intell Rev 44(4):467–508.  https://doi.org/10.1007/s10462-015-9433-y CrossRefGoogle Scholar
  33. 33.
    Shannon CE, Weaver W (1949) The mathematical theory of communication. University of Illinois Press, ChampaignzbMATHGoogle Scholar
  34. 34.
    Strang G, Nguyen T (1996) Wavelets and filter banks. Wellesley-Cambridge Press. https://books.google.com.br/books?id=Z76N_Ab5pp8C
  35. 35.
    Sun Y (2007) Cost-sensitive boosting for classification of imbalanced data. Pattern Recogn 40:3358–3378CrossRefGoogle Scholar
  36. 36.
    Chen J, Ma B, Cao H, Chen J, Fan Y, Li R, Wu W (2017) Updating initial labels from spectral graph by manifold regularization for saliency detection. Elsevier. Neurocomputing 266:79–90CrossRefGoogle Scholar
  37. 37.
    Wang H, Li Z, Li Y, Gupta BB, Choi C (2018) Visual saliency guided complex image retrieval. Pattern Recognition Letters,  https://doi.org/10.1016/j.patrec.2018.08.010
  38. 38.
    Zhang S, Wang H, Huang W, Zhang C (2018) Combining modified LBP and weighted SRC for palmprint recognition. SIViP, 1–8.  https://doi.org/10.1007/s11760-018-1246-4
  39. 39.
    Zhao Y, Zhang L, Li P, Huang B (2007) Classification of high spatial resolution imagery using improved gaussian Markov random-field-based texture features. IEEE Trans Geosci Remote Sensing 45:1458–1468.  https://doi.org/10.1109/tgrs.2007.892602 CrossRefGoogle Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Computing DepartmentFederal University of São CarlosSão CarlosBrazil

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