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

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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.

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References

  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

    Article  MATH  Google Scholar 

  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 repository

    Google Scholar 

  3. Besag J (1974) Spatial interaction and the statistical analysis of Lattice systems. J R Stat Soc Ser Spatial interaction B 36:192–236

    MathSciNet  MATH  Google Scholar 

  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

    Article  MathSciNet  Google Scholar 

  5. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20:273–297. https://doi.org/10.1023/A:1022627411411

    Article  MATH  Google Scholar 

  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

    Article  Google Scholar 

  7. Cohen J (1960) A coefficient of agreement for nominal scales. Educ Psychol Meas 20(1):37–46

    Article  Google Scholar 

  8. Congalton RG (1991) A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environ 37(1):35–46

    Article  Google Scholar 

  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. 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

    Article  Google Scholar 

  11. Emerson WC (1998) Multi-scale fractal analysis of image texture and pattern

  12. Guo G, Wang H, Bell D, Bi Y, Greer K (2003) KNN model-based approach in classification. Springer, Berlin, pp 986–996

    Google Scholar 

  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, 2

  14. Han J, Kamber M, Pei J (2006) Data mining: concepts and techniques. Seconded. Morgan Kaufmann Publishers, San Francisco

    MATH  Google Scholar 

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

    Article  Google Scholar 

  16. Haralick R, Shanmugam K, Dinstein I (1973) Texture features for image classification. IEEE Trans Syst Man Cybern, 3

  17. Hubel DH, Wiesel TN (1968) Receptive fields and functional architecture of monkey striate cortex. J Physiol 195.1:215–243

    Article  Google Scholar 

  18. Jensen A, Cour-Harbo A (2011) Ripples in mathematics: the discrete wavelet transform. Springer, Berlin. https://books.google.com.br/books?id=nMIPBwAAQBAJ

    MATH  Google Scholar 

  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

    Article  Google Scholar 

  20. Kass RE (1989) The geometry of asymptotic inference. Statist Sci 4:188–219. https://doi.org/10.1214/ss/1177012480

    Article  MathSciNet  MATH  Google Scholar 

  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

    Article  Google Scholar 

  22. Kwitt R, Meerwald P (2017) Salzburg texture image database, online Available: http://www.wavelab.at/sources/STex/

  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. 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 gruyter

    Article  Google Scholar 

  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

    Article  MathSciNet  MATH  Google Scholar 

  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

    Article  Google Scholar 

  27. Musgrave FK, Peachey D, Perlin K, Worley S (1994) Texturing and modeling: a procedural approach. Academic Press Professional, Inc., San Diego

    Google Scholar 

  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 701–706

  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

    Book  Google Scholar 

  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

    Article  Google Scholar 

  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 3166–3173

  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

    Article  Google Scholar 

  33. Shannon CE, Weaver W (1949) The mathematical theory of communication. University of Illinois Press, Champaign

    MATH  Google Scholar 

  34. Strang G, Nguyen T (1996) Wavelets and filter banks. Wellesley-Cambridge Press. https://books.google.com.br/books?id=Z76N_Ab5pp8C

  35. Sun Y (2007) Cost-sensitive boosting for classification of imbalanced data. Pattern Recogn 40:3358–3378

    Article  Google Scholar 

  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 266:79–90

    Article  Google Scholar 

  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. 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. 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

    Article  Google Scholar 

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Acknowledgements

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

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  1. Computing Department, Federal University of São Carlos, São Carlos, SP, Brazil

    Cédrick Bamba Nsimba & Alexandre Levada

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  1. Cédrick Bamba Nsimba
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  2. Alexandre Levada
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Correspondence to Cédrick Bamba Nsimba.

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Nsimba, C.B., Levada, A. An information-theoretic wavelet-based texture descriptor using Gaussian Markov random field models. Multimed Tools Appl 78, 31959–31986 (2019). https://doi.org/10.1007/s11042-019-07916-3

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  • DOI: https://doi.org/10.1007/s11042-019-07916-3

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