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Image classification based on Markov random field models with Jeffreys divergence

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  • Nishii, Ryuei
  • Eguchi, Shinto

Abstract

This paper considers image classification based on a Markov random field (MRF), where the random field proposed here adopts Jeffreys divergence between category-specific probability densities. The classification method based on the proposed MRF is shown to be an extension of Switzer's soothing method, which is applied in remote sensing and geospatial communities. Furthermore, the exact error rates due to the proposed and Switzer's methods are obtained under the simple setup, and several properties are derived. Our method is applied to a benchmark data set of image classification, and exhibits a good performance in comparison with conventional methods.

Suggested Citation

  • Nishii, Ryuei & Eguchi, Shinto, 2006. "Image classification based on Markov random field models with Jeffreys divergence," Journal of Multivariate Analysis, Elsevier, vol. 97(9), pages 1997-2008, October.
    • Handle: RePEc:eee:jmvana:v:97:y:2006:i:9:p:1997-2008
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    References listed on IDEAS

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    1. Mardia, K. V., 1988. "Multi-dimensional multivariate Gaussian Markov random fields with application to image processing," Journal of Multivariate Analysis, Elsevier, vol. 24(2), pages 265-284, February.
    2. Shinto Eguchi, 2002. "A class of logistic-type discriminant functions," Biometrika, Biometrika Trust, vol. 89(1), pages 1-22, March.
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    Cited by:

    1. Selma Metzner & Gerd Wübbeler & Clemens Elster, 2019. "Approximate large-scale Bayesian spatial modeling with application to quantitative magnetic resonance imaging," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 103(3), pages 333-355, September.

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