Image classification based on Markov random field models with Jeffreys divergence

Ryuei Nishii, Shinto Eguchi

研究成果: ジャーナルへの寄稿学術誌査読

16 被引用数 (Scopus)

抄録

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.

本文言語英語
ページ(範囲)1997-2008
ページ数12
ジャーナルJournal of Multivariate Analysis
97
9
DOI
出版ステータス出版済み - 10月 2006

!!!All Science Journal Classification (ASJC) codes

  • 統計学および確率
  • 数値解析
  • 統計学、確率および不確実性

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