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Distributional-based texture classification using non-parametric statistics

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Abstract

Texture classification is an important problem in image analysis. In the present study, an efficient strategy for classifying texture images is introduced and examined within a distributional-statistical framework. Our approach incorporates the multivariate Wald–Wolfowitz test (WW-test), a non-parametric statistical test that measures the similarity between two different sets of multivariate data, which is utilized here for comparing texture distributions. By summarizing the texture information using standard feature extraction methodologies, the similarity measure provides a comprehensive estimate of the match between different images based on graph theory. The proposed “distributional metric” is shown to handle efficiently the texture-space dimensionality and the limited sample size drawn from a given image. The experimental results, from the application on a typical texture database, clearly demonstrate the effectiveness of our approach and its superiority over other well-established texture distribution (dis)similarity metrics. In addition, its performance is used to evaluate several approaches for texture representation. Even though the classification results are obtained on grayscale images, a direct extension to color-based ones can be straightforward.

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Notes

  1. The Oulu texture database was downloaded from: http://www.outex.oulu.fi/temp/

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Acknowledgments

This work was supported by the National Ministry of Education, under the project entitled “Soft computing techniques for multichannel processing”, in the context of the program “Archimidis”.

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Authors and Affiliations

  1. Electronics Laboratory, Department of Physics, University of Patras, Patras, 26500, Greece

    Vasileios K. Pothos, Christos Theoharatos, Evangelos Zygouris & George Economou

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  1. Vasileios K. Pothos
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  2. Christos Theoharatos
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  3. Evangelos Zygouris
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  4. George Economou
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Correspondence to George Economou.

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Pothos, V.K., Theoharatos, C., Zygouris, E. et al. Distributional-based texture classification using non-parametric statistics. Pattern Anal Applic 11, 117–129 (2008). https://doi.org/10.1007/s10044-007-0083-9

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