Image classification based on Markov random field models with Jeffreys divergence

Ryuei Nishii and Shinto Eguchi

Journal of Multivariate Analysis, 2006, vol. 97, issue 9, 1997-2008

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.

Keywords: Bayes; estimate; Discriminant; analysis; Image; analysis; Kullback-Leibler; information (search for similar items in EconPapers)
Date: 2006
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Citations: View citations in EconPapers (1)

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