Image classification based on Markov random field models with Jeffreys divergence

Authors:
Ryuei Nishii;Shinto Eguchi
Affiliations:
Faculty of Mathematics, Kyushu University, Hakozaki, Higashiku Fukuoka, Japan;Institute of Statistical Mathematics, Minami-Azabu, Minatoku Tokyo, Japan
Venue:
Journal of Multivariate Analysis - Special issue dedicated to Professor Yasunori Fujikoshi
Year:
2006

Citing 3
Cited 1

Multi-dimensional multivariate Gaussian Markov random fields with application to image processing

Journal of Multivariate Analysis
Statistical Pattern Recognition: A Review

IEEE Transactions on Pattern Analysis and Machine Intelligence
Gauss-Markov Measure Field Models for Low-Level Vision

IEEE Transactions on Pattern Analysis and Machine Intelligence

RETRACTED: Application of Bayes linear discriminant functions in image classification

Pattern Recognition Letters

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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.