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A new subspace learning method in Fourier domain for texture classification

8 years 11 months ago
A new subspace learning method in Fourier domain for texture classification
This paper proposes a new texture classification approach. There are two main contributions in the proposed method. First, input texture images are transformed to the composite Fourier domain (CFD) by using both the local and global Fourier transforms. The composite Fourier domain is rotation invariant and preserves the contextual information for the texture images in the original spatial domain. Second, the nullspace based linear discriminant analysis (nLDA) is adopted to find the optimal representations of the texture images in the composite Fourier domain. This paper proposes a systematic way to cooperate subspace learning methods for texture classification in the frequency domain, which cannot be directly applied in the spatial domain for texture classification. The proposed method is evaluated on both the Brodatz and CUReT databases and compared with several state-of-the-art texture classification approaches. Experimental results show that the proposed method achieves the highest...
Shu Liao, Albert C. S. Chung
Added 12 Feb 2011
Updated 12 Feb 2011
Type Journal
Year 2010
Where ICIP
Authors Shu Liao, Albert C. S. Chung
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