Robust texture features based on undecimated dual-tree complex wavelets and local magnitude binary patterns

Image degradation due to illumination change, blur and noise can have a significant influence on classification performance, and yet no descriptors that perform well under these conditions exist. We propose a novel method for obtaining texture features, robust to these distortions, based on an undecimated dual-tree complex wavelet transform (UDT-CWT). As the UDT-CWT provides a local spatial relationship between scales, we can straightforwardly create bit-planes of the images representing local phases of wavelet coefficients. Magnitudes of the UDT-CWT are captured via a local binary pattern (LBP), after discarding some of the finest scales that are most affected by the blur and noise. A histogram of the resulting binary code words then forms the features used in texture classification. Results show that our approach outperforms existing methods, that claim to be invariant to feature degradations.