Texture classification using wavelet maxima representation

Wenjian Wang; William G. Wee

doi:10.1117/12.304888

26 March 1998 Texture classification using wavelet maxima representation

Wenjian Wang, William G. Wee

Proceedings Volume 3391, Wavelet Applications V; (1998) https://doi.org/10.1117/12.304888
Event: Aerospace/Defense Sensing and Controls, 1998, Orlando, FL, United States

Abstract

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.

Citation Download Citation

Wenjian Wang and William G. Wee "Texture classification using wavelet maxima representation", Proc. SPIE 3391, Wavelet Applications V, (26 March 1998); https://doi.org/10.1117/12.304888

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