Title :
Comparison and fusion of multiresolution features for texture classification
Author :
Li, Shu-Tao ; Li, Yi ; Wang, Yao-Nan
Author_Institution :
Coll. of Electr. & Inf. Eng., Hunan Univ., Changsha, China
Abstract :
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.
Keywords :
image classification; image resolution; image texture; support vector machines; wavelet transforms; Gabor wavelet; dyadic wavelet frame; image texture classification; multiresolution features; steerable pyramid; support vector machines; Educational institutions; Filter bank; Gabor filters; Low pass filters; Signal processing algorithms; Signal resolution; Spatial resolution; Support vector machine classification; Support vector machines; Wavelet transforms;
Conference_Titel :
Machine Learning and Cybernetics, 2004. Proceedings of 2004 International Conference on
Print_ISBN :
0-7803-8403-2
DOI :
10.1109/ICMLC.2004.1380449