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Decision Fusion Based Unsupervised Texture Image Segmentation

  • Hua Zhong
  • Licheng Jiao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3801)

Abstract

A decision fusion based method is proposed to improve unsupervised image segmentation. After the step of cluster label adjustment, each kind of texture is fixed with the same label. Then three simple fusion operators are applied according to the knowledge of multi-classifier fusion. Compared with feature fusion, decision fusion can combine the advantages of different features more intuitively and heuristically. Experimental results on textures and synthetic aperture radar (SAR) image demonstrate its superiority over feature fusion on removing the impact of noise feature and preserving the detail.

Keywords

Window Size Synthetic Aperture Radar Synthetic Aperture Radar Image True Segmentation Feature Fusion 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Hua Zhong
    • 1
  • Licheng Jiao
    • 1
  1. 1.Institute of Intelligent Information ProcessingXidian UniversityXi’anChina

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