Comparison and fusion of multiresolution features for texture classification
Shu-Tao Li; Yi Li; Yao-Nan Wang
Machine Learning and Cybernetics, 2004. Proceedings of 2004 International Conference on
Volume 6, Issue , 26-29 Aug. 2004 Page(s): 3684 - 3688 vol.6
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Summary: We investigate the texture classification problem with individual and combined multiresolution features, i.e., dyadic wavelet frame, Gabor wavelet, and steerable pyramid. The 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, and the dyadic wavelet significantly lags them. Experimental results on fused features demonstrate the combination of two feature sets always outperform each method individually. And the fused feature sets of multi-orientation decompositions and stationary wavelet achieve the highest accuracy.

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