Comparison and fusion of multiresolution features for texture classification

In this paper, we investigate the texture classification problem with individual and combined multiresolution features, i.e., dyadic wavelet, wavelet frame, Gabor wavelet, and steerable pyramid. Support vector machines are used as classifiers. The experimental results show that the steerable pyramid and Gabor wavelet classify texture images with the highest accuracy, the wavelet frame follows them, the dyadic wavelet significantly lags behind. Experimental results on fused features demonstrated the combination of two feature sets always outperformed each method individually. And the fused feature sets of multi-orientation decompositions and stationary wavelet achieve the highest accuracy.