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An Application of a Pseudo-Parabolic Modeling to Texture Image Recognition

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Computational Science – ICCS 2021 (ICCS 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12743))

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Abstract

In this work, we present a novel methodology for texture image recognition using a partial differential equation modeling. More specifically, we employ the pseudo-parabolic equation to provide a dynamics to the digital image representation and collect local descriptors from those images evolving in time. For the local descriptors we employ the magnitude and signal binary patterns and a simple histogram of these features was capable of achieving promising results in a classification task. We compare the accuracy over well established benchmark texture databases and the results demonstrate competitiveness, even with the most modern deep learning approaches. The achieved results open space for future investigation on this type of modeling for image analysis, especially when there is no large amount of data for training deep learning models and therefore model-based approaches arise as suitable alternatives.

Supported by São Paulo Research Foundation (FAPESP), National Council for Scientific and Technological Development, Brazil (CNPq), and PETROBRAS - Brazil.

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References

  1. Abreu, E., Vieira, J.: Computing numerical solutions of the pseudo-parabolic Buckley-Leverett equation with dynamic capillary pressure. Math. Comput. Simul. 137, 29–48 (2017)

    Article  MathSciNet  Google Scholar 

  2. Abreu, E., Ferraz, P., Vieira, J.: Numerical resolution of a pseudo-parabolic Buckley-Leverett model with gravity and dynamic capillary pressure in heterogeneous porous media. J. Comput. Phys. 411 (2020). https://doi.org/10.1016/j.jcp.2020.109395. http://www.sciencedirect.com/science/article/pii/S0021999120301698

  3. Ahonen, T., Matas, J., He, C., Pietikäinen, M.: Rotation invariant image description with local binary pattern histogram fourier features. In: Salberg, A.-B., Hardeberg, J.Y., Jenssen, R. (eds.) SCIA 2009. LNCS, vol. 5575, pp. 61–70. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-02230-2_7

    Chapter  Google Scholar 

  4. Barros Neiva, M., Guidotti, P., Bruno, O.M.: Enhancing LBP by preprocessing via anisotropic diffusion. Int. J. Mod. Phys. C 29(08), 1850071 (2018)

    Google Scholar 

  5. Bruna, J., Mallat, S.: Invariant scattering convolution networks. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1872–1886 (2013)

    Article  Google Scholar 

  6. Catté, F., Lions, P.L., Morel, J.M., Coll, T.: Image selective smoothing and edge detection by nonlinear diffusion. SIAM J. Numer. Anal. 29(1), 182–193 (1992)

    Article  MathSciNet  Google Scholar 

  7. Chan, T., Jia, K., Gao, S., Lu, J., Zeng, Z., Ma, Y.: PCANet: a simple deep learning baseline for image classification? IEEE Trans. Image Process. 24(12), 5017–5032 (2015)

    Article  MathSciNet  Google Scholar 

  8. Cimpoi, M., Maji, S., Kokkinos, I., Mohamed, S., Vedaldi, A.: Describing textures in the wild. In: Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014, pp. 3606–3613. IEEE Computer Society, Washington, DC (2014)

    Google Scholar 

  9. 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 

  10. Cuesta, C., Pop, I.: Numerical schemes for a pseudo-parabolic burgers equation: discontinuous data and long-time behaviour. J. Comput. Appl. Math. 224, 269–283 (2009)

    Article  MathSciNet  Google Scholar 

  11. Dhivyaa, C.R., Sangeetha, K., Balamurugan, M., Amaran, S., Vetriselvi, T., Johnpaul, P.: Skin lesion classification using decision trees and random forest algorithms. J. Ambient Intell. Humaniz. Comput. 1, 1–13 (2020). https://link.springer.com/article/10.1007/s12652-020-02675-8

  12. Fisher, R.A.: The use of multiple measurements in taxonomic problems. Ann. Eugen. 7(2), 179–188 (1936)

    Article  Google Scholar 

  13. Guo, Z., Zhang, L., Zhang, D.: A completed modeling of local binary pattern operator for texture classification. Trans. Image Process. 19(6), 1657–1663 (2010)

    Article  MathSciNet  Google Scholar 

  14. 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 

  15. Jain, D.K., Zhang, Z., Huang, K.: Multi angle optimal pattern-based deep learning for automatic facial expression recognition. Pattern Recogn. Lett. 139, 157–165 (2020)

    Article  Google Scholar 

  16. Kannala, J., Rahtu, E.: BSIF: binarized statistical image features. In: ICPR, pp. 1363–1366. IEEE Computer Society (2012)

    Google Scholar 

  17. Koenderink, J.J.: The structure of images. Biol. Cybern. 50(5), 363–370 (1984)

    Article  MathSciNet  Google Scholar 

  18. Lazebnik, S., Schmid, C., Ponce, J.: A sparse texture representation using local affine regions. IEEE Trans. Pattern Anal. Mach. Intell. 27(8), 1265–1278 (2005)

    Article  Google Scholar 

  19. Lin, J., Pappas, T.N.: Structural texture similarity for material recognition. In: 2019 IEEE International Conference on Image Processing (ICIP), pp. 4424–4428. IEEE International Conference on Image Processing ICIP, Inst Elect & Elect Engineers; Inst Elect & Elect Engineers Signal Proc Soc (2019), 26th IEEE International Conference on Image Processing (ICIP), Taipei, TAIWAN, 22–25 September 2019

    Google Scholar 

  20. Liu, L., Zhao, L., Long, Y., Kuang, G., Fieguth, P.: Extended local binary patterns for texture classification. Image Vision Comput. 30(2), 86–99 (2012)

    Article  Google Scholar 

  21. Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)

    Article  Google Scholar 

  22. Pearson, F.K.: LIII. On lines and planes of closest fit to systems of points in space. London Edinburgh Dublin Philos. Mag. J. Sci. 2(11), 559–572 (1901)

    Google Scholar 

  23. Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Anal. Mach. Intell. 12(7), 629–639 (1990)

    Article  Google Scholar 

  24. Robert Singh, K., Chaudhury, S.: Comparative analysis of texture feature extraction techniques for rice grain classification. IET Image Process. 14(11), 2532–2540 (2020)

    Article  Google Scholar 

  25. Varma, M., Zisserman, A.: A statistical approach to texture classification from single images. Int. J. Comput. Vision 62(1), 61–81 (2005)

    Article  Google Scholar 

  26. Varma, M., Zisserman, A.: A statistical approach to material classification using image patch exemplars. IEEE Trans. Pattern Anal. Mach. Intell. 31(11), 2032–2047 (2009)

    Article  Google Scholar 

  27. Verma, V., Muttoo, S.K., Singh, V.B.: Multiclass malware classification via first- and second-order texture statistics. Comput. Secur. 97, 101895 (2020)

    Article  Google Scholar 

  28. Vieira, J., Abreu, E.: Numerical modeling of the two-phase flow in porous media with dynamic capillary pressure. Ph.D. thesis, University of Campinas, Campinas, SP, Brazil (2018)

    Google Scholar 

  29. Vieira, J., Abreu, E., Florindo, J.B.: Texture image classification based on a pseudo-parabolic diffusion model (2020). https://arxiv.org/abs/2011.07173

  30. Weickert, J.: A review of nonlinear diffusion filtering. In: ter Haar Romeny, B., Florack, L., Koenderink, J., Viergever, M. (eds.) Scale-Space 1997. LNCS, vol. 1252, pp. 1–28. Springer, Heidelberg (1997). https://doi.org/10.1007/3-540-63167-4_37

    Chapter  Google Scholar 

  31. Witkin, A.P.: Scale-space filtering. In: Proceedings of the Eighth International Joint Conference on Artificial Intelligence, IJCAI 1983, vol. 2, pp. 1019–1022. Morgan Kaufmann Publishers Inc., San Francisco (1983)

    Google Scholar 

  32. Xu, Y., Ji, H., Fermüller, C.: Viewpoint invariant texture description using fractal analysis. Int. J. Comput. Vision 83(1), 85–100 (2009)

    Article  Google Scholar 

  33. Zhao, G., Wang, X., Cheng, Y.: Hyperspectral image classification based on local binary pattern and broad learning system. Int. J. Remote Sens. 41(24), 9393–9417 (2020)

    Article  Google Scholar 

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Acknowledgements

J. B. Florindo gratefully acknowledges the financial support of São Paulo Research Foundation (FAPESP) (Grant #2016/16060-0) and from National Council for Scientific and Technological Development, Brazil (CNPq) (Grants #301480/2016-8 and #423292/2018-8). E. Abreu gratefully acknowledges the financial support of São Paulo Research Foundation (FAPESP) (Grant #2019/20991-8), from National Council for Scientific and Technological Development - Brazil (CNPq) (Grant #2 306385/2019-8) and PETROBRAS - Brazil (Grant #2015/00398-0). E. Abreu and J. B. Florindo also gratefully acknowledge the financial support of Red Iberoamericana de Investigadores en Matemáticas Aplicadas a Datos (MathData).

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Authors and Affiliations

  1. Institute of Mathematics, Statistics and Scientific Computing, University of Campinas, Rua Sérgio Buarque de Holanda, 651, Cidade Universitária “Zeferino Vaz” - Distr. Barão Geraldo, 13083-859, Campinas, SP, Brazil

    Joao B. Florindo & Eduardo Abreu

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  1. Joao B. Florindo
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  2. Eduardo Abreu
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Correspondence to Joao B. Florindo .

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Editors and Affiliations

  1. AGH University of Science and Technology, Krakow, Poland

    Maciej Paszynski

  2. Ludwig-Maximilians-Universität München, Munich, Germany

    Dieter Kranzlmüller

  3. University of Amsterdam, Amsterdam, The Netherlands

    Valeria V. Krzhizhanovskaya

  4. University of Tennessee at Knoxville, Knoxville, TN, USA

    Jack J. Dongarra

  5. University of Amsterdam, Amsterdam, The Netherlands

    Peter M. A. Sloot

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Florindo, J.B., Abreu, E. (2021). An Application of a Pseudo-Parabolic Modeling to Texture Image Recognition. In: Paszynski, M., Kranzlmüller, D., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2021. ICCS 2021. Lecture Notes in Computer Science(), vol 12743. Springer, Cham. https://doi.org/10.1007/978-3-030-77964-1_30

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  • DOI: https://doi.org/10.1007/978-3-030-77964-1_30

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