Multiresolution image parametrization for improving texture classification

Luka Šajn and Igor Kononenko (2008) Multiresolution image parametrization for improving texture classification. EURASIP journal on advances in signal processing . ISSN 1687-6172

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Abstract

In the paper an innovative alternative to automatic image parametrization on multiple resolutions, based on texture description with specialized association rules, and image evalu- ation with machine learning methods is presented. The algorithm ArTex for parameterizing textures with association rules belonging to structural parametrization algorithms was de- veloped. In order to improve the classification accuracy a multi-resolution approach is used. The algorithm ARes for finding more informative resolutions based on the SIFT algorithm is described. The presented algorithms are evaluated on several public domains and the results are compared to other well-known parametrization algorithms belonging to statistical and spectral parametrization algorithms. Significant improvement of classification results was observed when combining parametrization attributes at several image resolutions for most parametrization algorithms. Our results show that multi-resolution image parametrization should be considered when improvement of classification accuracy in textural domains is required. These resolutions have to be selected carefully and may depend on the domain itself.

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