Rotation Invariant Texture Classification with Ridgelet Transform and Fourier Transform

The features extracted from traditional wavelet transform have been successfully applied to texture classification. However, most wavelet features are not invariant to image rotation. This paper proposes a new rotation invariant feature based on the combination of ridgelet, a directional non-separable wavelet transform, and Fourier transforms. The ridgelet transform is applied to the rotated image, transforming the rotation angle to shifts in the ridgelet domain. Changes caused by the shift is eliminated by using the magnitude of the Fourier transform in the ridgelet domain. The rotation invariance is proved theoretically and verified by experimental results.