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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
  • 71 Downloads

Abstract

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.

Keywords

Texture classification Feature extraction Gabor filtering Multi-resolution analysis 

Notes

Acknowledgements

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.

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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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