A new subspace learning method in Fourier domain for texture classification

Abstract

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 null-space 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 classification rate among all the compared methods.