Image classification based on Markov random

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 smoothing method, which is applied in re-mote 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 im-age classification, and exhibits a good performance in comparison with conventional methods. Key words: Bayes estimate; discriminant analysis; image analysis; Kullback-Leibler information