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.
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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.
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
Xia, Z., et al.: Fingerprint liveness detection using gradient-based texture features. Signal Image Video Process. 11(2), 381–388 (2017)CrossRefGoogle Scholar
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
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
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
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
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