Image classification based on Markov random field models with Jeffreys divergence. (English) Zbl 1101.62086
Summary: This paper considers image classification based on a Markov random field (MRF), where the random field proposed here adopts H. Jeffreys’ [An invariant form for the prior probability in estimation problems. Proc. R. Soc. Lond., Ser. A 186, 453–461 (1946)] divergence between category-specific probability densities. The classification method based on the proposed MRF is shown to be an extension of P. Switzer’s soothing method [Extensions of linear discriminant analysis for statistical classification of remotely sensed satellite imagery. Math. Geol. 12, 367–376 (1980)], which is applied in remote sensing and geospatial communities. Furthermore, the exact error rates due to the proposed and Switzer’s method are obtained under a 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.
MSC:
| 62M40 | Random fields; image analysis |
| 62H30 | Classification and discrimination; cluster analysis (statistical aspects) |
| 62H35 | Image analysis in multivariate analysis |
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