We study the texture classification problem, i.e., allocating an observed texture sample to one of known texture classes. We propose a multiresolution approach based on wavelet maxima representation for texture classification. First, a multiscale wavelet maxima representation of the image is generated by a wavelet transform. Energy and entropy are calculated and weighted at each scale. These features form a feature vector of the image. A minimum- distance classifier is used in texture classification. Classification experiments with 18 Bordatz texture indicates that this method is both translation and rotation invariant and achieves 99 percent classification accuracy. Noise sensitivity analysis shows that this method has excellent performance in noisy situation. Finally a detailed comparison of various wavelet transform based texture classification methods is provided.

Date Published: 26 March 1998
PDF: 8 pages
Proc. SPIE 3391, Wavelet Applications V, (26 March 1998); doi: 10.1117/12.304888

Published in SPIE Proceedings Vol. 3391:
Wavelet Applications V
Harold H. Szu, Editor(s)