Signal, Image and Video Processing

, Volume 13, Issue 1, pp 163–170 | Cite as

Texture classification using multi-resolution global and local Gabor features in pyramid space

  • Junmin WangEmail author
  • Yangyu Fan
  • Zuhe Li
  • Tao Lei
Original Paper


Texture classification plays an important role in computer vision, and Gabor filtering (GF) is a promising direction of texture classification for its desirable characteristics. However, traditional GF methods are too coarse to achieve satisfactory classification performance. To address this problem, this paper presents an effective texture classification method by combing multi-resolution global and local Gabor features in pyramid space. First, a pyramid space for each image is constructed via upsampling and downsampling to represent the images with different resolutions. Second, GF is applied to each image at different scales and orientations, and then the magnitude and phase components of filtered images are calculated. Third, the global and local Gabor features are extracted, where the global Gabor feature is represented by the mean and variance of the magnitude component, and the local Gabor feature is represented by the joint coding of both magnitude and phase components in a histogram form. Finally, the fusion of global and local Gabor features and the texture classification are implemented in the framework of nearest subspace classifier. Experimental results on CUReT and KTH-TIPS databases demonstrate that the proposed method significantly improves the performance of GF-based texture classification methods.


Texture classification Feature extraction Gabor filtering Multi-resolution analysis 



The authors would like to express their gratitude to the anonymous reviewers for their constructive comments and suggestions. The authors sincerely thank Zhenhua Guo, Yang Zhao, Jin Xie, Rakesh Mehta and MVG for sharing the source codes of CLBP, CLBC, TEISF, DRLBP and LBP, respectively. This work was supported by the National Natural Science Foundation of China under Grant 61702462.


  1. 1.
    Gilanie, G., et al.: Classification of normal and abnormal brain MRI slices using Gabor texture and support vector machines. Signal Image Video Process. 12(3), 479–487 (2018)CrossRefGoogle Scholar
  2. 2.
    Xia, Z., et al.: Fingerprint liveness detection using gradient-based texture features. Signal Image Video Process. 11(2), 381–388 (2017)CrossRefGoogle Scholar
  3. 3.
    Yu, L., et al.: Multi-trend binary code descriptor: a novel local texture feature descriptor for image retrieval. Signal Image Video Process. 12(2), 247–254 (2018)CrossRefGoogle Scholar
  4. 4.
    Medouakh, S., et al.: Improved object tracking via joint color-LPQ texture histogram based mean shift algorithm. Signal Image Video Process. 12(3), 583–590 (2018)CrossRefGoogle Scholar
  5. 5.
    Wang, J., Fan, Y., Li, N.: Combining fine texture and coarse color features for color texture classification. J. Electron. Imaging 26(6), 063027 (2017)Google Scholar
  6. 6.
    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)CrossRefzbMATHGoogle Scholar
  7. 7.
    Guo, Z.H., Zhang, L., Zhang, D.: A completed modeling of local binary pattern operator for texture classification. IEEE Trans. Image Process. 19(6), 1657–1663 (2010)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Zhao, Y., Huang, D.-S., Jia, W.: Completed local binary count for rotation invariant texture classification. IEEE Trans. Image Process. 21(10), 4492–4497 (2012)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Guo, Z., et al.: Robust texture image representation by scale selective local binary patterns. IEEE Trans. Image Process. 25(2), 687–699 (2016)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Varma, M., Zisserman, A.: A statistical approach to texture classification from single images. Int. J. Comput. Vis. 62(1/2), 61–81 (2005)CrossRefGoogle Scholar
  11. 11.
    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)CrossRefGoogle Scholar
  12. 12.
    Xie, J., et al.: Effective texture classification by texton encoding induced statistical features. Pattern Recognit. 48, 447–457 (2015)CrossRefGoogle Scholar
  13. 13.
    Manjunath, B.S., Ma, W.Y.: Texture features for browsing and retrieval of image data. IEEE Trans. Pattern Anal. Mach. Intell. 18(8), 837–842 (1996)CrossRefGoogle Scholar
  14. 14.
    Arivazhagan, S., et al.: Texture classification using Gabor wavelets based rotation invariant features. Pattern Recognit. Lett. 27, 1976–1982 (2006)CrossRefGoogle Scholar
  15. 15.
    Han, J., Ma, K.-K.: Rotation-invariant and scale-invariant Gabor features for texture image retrieval. Image Vis. Comput. 25, 1474–1481 (2007)CrossRefGoogle Scholar
  16. 16.
    Hadizadeh, H.: Multi-resolution local Gabor wavelets binary patterns for gray-scale texture description. Pattern Recognit. Lett. 65, 163–169 (2015)CrossRefGoogle Scholar
  17. 17.
    Hong, X., et al.: Combining LBP difference and feature correlation for texture description. IEEE Trans. Image Process. 23(6), 2557–2568 (2014)MathSciNetCrossRefzbMATHGoogle Scholar
  18. 18.
    Mehta, R., Egiazarian, K.: Dominant rotated local binary patterns (DRLBP) for texture classification. Pattern Recognit. Lett. 71, 16–22 (2016)CrossRefGoogle Scholar
  19. 19.
    Duan, Y., et al.: Learning rotation-invariant local binary descriptor. IEEE Trans. Image Process. 26(8), 3636–3651 (2017)MathSciNetGoogle Scholar
  20. 20.
    Semwal, V.B., et al.: Robust and accurate feature selection for humanoid push recovery and classification: deep learning approach. Neural Comput. Appl. 28, 565–574 (2017)CrossRefGoogle Scholar
  21. 21.
    Basu, S., et al.: Deep neural networks for texture classification—a theoretical analysis. Neural Netw. 97, 173–182 (2018)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Electronics and InformationNorthwestern Polytechnical UniversityXi’anChina
  2. 2.School of Information EngineeringPingdingshan UniversityPingdingshanChina
  3. 3.College of Electronical and Information EngineeringShaanxi University of Science and TechnologyXi’anChina

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