Rotation-invariant texture recognition by rotation compensation and wavelet analysis

A new rotation-invariant wavelet-based texture recognition scheme is proposed. In the previous rotation-invariant approaches, the focus is on adapting the wavelet transform or filter to rotated texture. In our approach, instead, we estimate the rotation of the texture with respect to some reference orientation, and then rotate the texture image back to the reference orientation before applying the wavelet analysis to extract features. With such rotation compensation, even very simple features (such as 1-level DWT and the subband energy) can be effective in achieving high classification accuracy as we demonstrate through our experiments.