Texture classification via stationary-wavelet based contourlet transform

Authors:
Ying Hu;Biao Hou;Shuang Wang;Licheng Jiao
Affiliations:
Institute of Intelligent Information Processing and National Key Lab for Radar Signal Processing, Xidian University, Xi'an, China;Institute of Intelligent Information Processing and National Key Lab for Radar Signal Processing, Xidian University, Xi'an, China;Institute of Intelligent Information Processing and National Key Lab for Radar Signal Processing, Xidian University, Xi'an, China;Institute of Intelligent Information Processing and National Key Lab for Radar Signal Processing, Xidian University, Xi'an, China
Venue:
IWICPAS'06 Proceedings of the 2006 Advances in Machine Vision, Image Processing, and Pattern Analysis international conference on Intelligent Computing in Pattern Analysis/Synthesis
Year:
2006

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Abstract

A directional multiresolution approach was proposed for texture analysis and classification based on a modified contourlet transform named the stationary wavelet-based contourlet transform (SWBCT). In the phase for extracting features after the decomposition, energy measures, Hu moments and co-occurrence matrices were calculated respectively. The progressive texture classification algorithm had better performance compared with several other methods using wavelet, stationary wavelet, brushlet, contourlet and Gabor filters. Moreover, in the case that there are only small scale samples for training, our method can also obtain a satisfactory result.