Machine Vision and Applications

, Volume 29, Issue 3, pp 455–466 | Cite as

A new approach for rotation-invariant and noise-resistant texture analysis and classification

  • Mohammad Mahdi Feraidooni
  • Davood Gharavian
Original Paper


The analysis and classification of images, such as texture images, is one of the substantial and important fields in image processing. Due to destructive effects of image rotation and noise, the stability and efficiency of texture analysis and classification methods are an important research area. In this paper, a new method for texture analysis and classification has been proposed which is based on a particular combination of wavelet, ridgelet and Fourier transforms as well as support vector machine. The proposed method has been evaluated for 13 texture datasets produced by three original datasets containing 25 and 111 original textures from Brodatz database and 24 original textures from OUTEX database. These datasets comprise 415584 and 93600 rotated noise-free and noisy texture images for Brodatz database and also 49920 noisy and 4320 noise-free texture images for OUTEX database, respectively. Simulation results demonstrate the capability, efficiency and also stability of the proposed method especially for real-time rotation-invariant and noise-resistant texture analysis and classification.


Texture analysis Rotation and noise invariant Wavelet transform Ridgelet transform Support vector machine Computational time 


  1. 1.
    Arivazhagan, S., Ganesan, L., Subash Kumar, T.G.: Texture classification using ridgelet transform. Pattern Recognit. Lett. 27, 1875–1883 (2006)CrossRefGoogle Scholar
  2. 2.
    Avci, E., Sengur, A., Hanbay, D.: An optimum feature extraction method for texture classification. Expert Syst. Appl. 36, 6036–6043 (2009)CrossRefGoogle Scholar
  3. 3.
    Karahaliou, A.N., Boniatis, I.S.: Breast cancer diagnosis: analyzing texture of tissue surrounding microcalcifications. IEEE Trans. Inf. Technol. Biomed. 12(6), 731–738 (2008)CrossRefGoogle Scholar
  4. 4.
    Pun, C.M., Lee, M.C.: Log-polar wavelet energy signatures for rotation and scale invariant texture classification. IEEE Trans. Pattern Anal. Mach. Intell. 25, 590–603 (2003)CrossRefGoogle Scholar
  5. 5.
    Šuch, O., Barreda, S.: Bayes covariant multi-class classification. Pattern Recognit. Lett. 84, 99–106 (2016)CrossRefGoogle Scholar
  6. 6.
    Junior, J.J.D.M.S., Backes, A.R., Cortez, P.C.: Texture analysis and classification using shortest paths in graphs. Pattern Recognit. Lett. 34, 1314–1319 (2013)CrossRefGoogle Scholar
  7. 7.
    Fnliii, S., Liaig, N.: Classification of SAR image based on gray co-occurrence matrix and support vector machine. In: ICSP’O4 Proceedings, pp. 1385–1388 (2004)Google Scholar
  8. 8.
    Cheng, G., Zhou, P., Han, J.: Learning rotation-invariant convolutional neural networks for object detection in VHR optical remote sensing images. IEEE Trans. Geosci. Remote Sens. 54, 7405–7415 (2016)CrossRefGoogle Scholar
  9. 9.
    Tuceyran, M., Jain, A.K.: Texture analysis. In: Handbook of Pattern Recognition and Computer Vision, Chapter 2.1, 2nd edn, pp. 207–248. World Scientific Publishing Co. (1998)Google Scholar
  10. 10.
    Haralik, R.M., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. 3, 610–621 (1973)CrossRefGoogle Scholar
  11. 11.
    Conners, R.W., Harlow, C.A.: A theoretical comparison of texture algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 2, 204–222 (1980)CrossRefMATHGoogle Scholar
  12. 12.
    Petrou, M., Sevilla, P.G.: Image Processing Dealing with Texture, pp. 329–379 and 436–520. Wiley, Hoboken (2006)Google Scholar
  13. 13.
    Avci, E.: Comparison of wavelet families for texture classification by using wavelet packet entropy adaptive network based fuzzy inference system. Soft. Comput. 8, 225–231 (2008)CrossRefGoogle Scholar
  14. 14.
    Feraidooni, M.M., Ghofrani, S.: Texture analysis and classification dealing with rotation and different noises. World Appl. Sci. J. 14(2), 221–227 (2011)Google Scholar
  15. 15.
    Hadizadeh, H.: Multi-resolution local Gabor wavelets binay patterns for gray-scale texture description. Pattern Recognit. Lett. 65, 163–169 (2015)CrossRefGoogle Scholar
  16. 16.
    Lasmar, N.E., Baussard, A., Le Chenadec, G.: Asymmetric power distribution model of wavelet sub-bands for texture classification. Pattern Recognit. Lett. 52, 1–8 (2014)CrossRefGoogle Scholar
  17. 17.
    Dagher, I., Issa, S.: Subband effect of the wavelet fuzzy C-means features in texture classification. Image Vis. Comput. 30, 896–905 (2012)CrossRefGoogle Scholar
  18. 18.
    Jafari-Khouzani, K., Soltanian-Zadeh, H.: Radon transform orientation estimation for rotation invariant texture analysis. IEEE Trans. Pattern Anal. Mach. Intell. 27(6), 1004–1008 (2005)CrossRefGoogle Scholar
  19. 19.
    Qi, X., Shen, L., Zhao, G., Li, Q., Pietikäinen, M.: Globally rotation invariant multi-scale co-occurrence local binary pattern. Image Vis. Comput. 43, 16–26 (2015)CrossRefGoogle Scholar
  20. 20.
    Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24, 971–987 (2002)CrossRefMATHGoogle Scholar
  21. 21.
    Guo, Z., Zhang, L., Zhang, D.: Rotation invariant texture classification using LBP variance (LBPV) with global matching. Pattern Recognit. 43, 706–719 (2010)CrossRefMATHGoogle Scholar
  22. 22.
    Pan, W., Bui, T.D., Suen, C.Y.: Rotation invariant texture classification by ridgelet transform and frequency-orientation space decomposition. Sig. Process. 88, 189–199 (2008)CrossRefMATHGoogle Scholar
  23. 23.
    Qiao, Y.L., Song, C.Y., Zhao, C.H.: M-band ridgelet transform based texture classification. Pattern Recognit. Lett. 31, 1875–1883 (2010)Google Scholar
  24. 24.
    Dharmagunawardhana, C., Mahmoodi, S., Bennett, M., Niranjan, M.: Rotation invariant texture descriptors based on Gaussian Markov random fields for classification. Pattern Recognit. Lett. 69, 15–21 (2016)CrossRefGoogle Scholar
  25. 25.
    Ahmadvand, A., Daliri, M.R.: Invariant texture classification using a spatial filter bank in multi-resolution analysis. Image Vis. Comput. 45, 1–10 (2016)CrossRefGoogle Scholar
  26. 26.
    Shakoor, M.H., Boostani, R.: Extended mapping local binary pattern operator for texture classification. Int. J. Pattern Recognit. Artif. Intell. 31(6), 1750019, 1–22 (2017)Google Scholar
  27. 27.
    Feraidooni, M.M., Ghofrani, S.: Texture analysis and classification using a rotation and noise invariant method. In: The 3rd Int. Conference on Machine Vision, pp. 246–249 (2010)Google Scholar
  28. 28.
    Candes, E.J.: Ridgelets: theory and applications. Ph.D. Thesis, Technical Report, Department of Statistics, Stanford University (1998)Google Scholar
  29. 29.
    Candes, E.J., Donoho, D.L.: Ridgelets: a key to higher-dimensional intermittency? Philos. Trans. R. Soc. Lond. Ser. A. 357, 2495–2509 (1999)MathSciNetCrossRefMATHGoogle Scholar
  30. 30.
    Do, M.N., Vetterli, M.: The finite ridgelet transform for image representation. IEEE Trans. Image Process. 12, 16–28 (2003)MathSciNetCrossRefMATHGoogle Scholar
  31. 31.
    Chan, A.K., Peng, C.: Wavelets for Sensing Technologies, pp. 21–22. Artech House Inc., Norwood (2003)Google Scholar
  32. 32.
  33. 33.
    Brodatz, P.: Texture: A Photographic Album for Artists and Designers. Dover, New York (1966)Google Scholar
  34. 34.
    Outex Texture Database, Texture classification test suites, Center for Machine Vision Research, University of Oulu. (2007)

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  1. 1.Department of Telecommunications, Faculty of Electrical EngineeringShahid Beheshti University, G. C.TehranIran

Personalised recommendations