Contextual Image Segmentation based on AdaBoost and Markov Random Fields

Ryuei Nishii

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

AdaBoost, one of machine learning algorithms, is employed for classification of land-cover categories of geostatistical data. We assume that the posterior probability is given by the odds ratio due to loss functions. Further, landcover categories are assumed to follow Markov random fields (MRF). Then, we derive a classifier by combining two posteriors based on AdaBoost and MRF through the iterative conditional modes. Our procedure is applied to benchmark data sets provided by IEEE GRSS Data Fusion Committee and shows an excellent performance.

Original languageEnglish
Title of host publicationInternational Geoscience and Remote Sensing Symposium (IGARSS)
Pages3507-3509
Number of pages3
Volume6
Publication statusPublished - 2003
Externally publishedYes
Event2003 IGARSS: Learning From Earth's Shapes and Colours - Toulouse, France
Duration: Jul 21 2003Jul 25 2003

Other

Other2003 IGARSS: Learning From Earth's Shapes and Colours
CountryFrance
CityToulouse
Period7/21/037/25/03

All Science Journal Classification (ASJC) codes

  • Geology
  • Software

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  • Cite this

    Nishii, R. (2003). Contextual Image Segmentation based on AdaBoost and Markov Random Fields. In International Geoscience and Remote Sensing Symposium (IGARSS) (Vol. 6, pp. 3507-3509)