Robust supervised image classifiers by spatial AdaBoost based on robust loss functions

Ryuei Nishii, Shinto Eguchi

研究成果: ジャーナルへの寄稿会議記事査読

6 被引用数 (Scopus)


Spatial AdaBoost, proposed by Nishii and Eguchi (2005), is a machine learning technique for contextual supervised image classification of land-cover categories of geostatistical data. The method classifies a pixel through a convex combination of a log posterior probability at the current pixel and averages of log posteriors in various neighborhoods of the pixel. Weights for the log posteriors are tuned by minimizing the empirical risk based on the exponential loss function. It is known that the method classifies test data very fast and shows a similar performance to the Markov-random-field-based classifier in many cases. However, it is also known that the classifier gives a poor result for some data when the exponential loss puts too big penalty for misclassified data. In this paper, we consider a robust Spatial boosting method by taking a robust loss function instead of the exponential loss. For example, the logit loss function gives a linear penalty for misclassified data approximately, and is robust. The Spatial boosting methods are applied to artificial multispectral images and benchmark data sets. It is shown that Spatial LogitBoost based on the logit loss can classify the benchmark data very well even though Spatial AdaBoost based on the exponential loss failed to classify the data.

ジャーナルProceedings of SPIE - The International Society for Optical Engineering
出版ステータス出版済み - 12月 1 2005
イベントImage and Signal Processing for Remote Sensing XI - Bruges, ベルギー
継続期間: 9月 20 20059月 22 2005

!!!All Science Journal Classification (ASJC) codes

  • 電子材料、光学材料、および磁性材料
  • 凝縮系物理学
  • コンピュータ サイエンスの応用
  • 応用数学
  • 電子工学および電気工学


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