Texture classification using hierarchical linear discriminant space

Yousun Kang, Ken'ichi Morooka, Hiroshi Nagahashi

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

As a representative of the linear discriminant analysis, the Fisher method is most widely used in practice and it is very effective in two-class classification. However, when it is expanded to a multi-class classification problem, the precision of its discrimination may become worse. A main reason is an occurrence of overlapped distributions on the discriminant space built by Fisher criterion. In order to take such overlaps among classes into consideration, our approach builds a new discriminant space by hierarchically classifying the overlapped classes. In this paper, we propose a new hierarchical discriminant analysis for texture classification. We divide the discriminant space into subspaces by recursively grouping the overlapped classes. In the experiment, texture images from many classes are classified based on the proposed method. We show the outstanding result compared with the conventional Fisher method.

Original languageEnglish
Pages (from-to)2380-2387
Number of pages8
JournalIEICE Transactions on Information and Systems
VolumeE88-D
Issue number10
DOIs
Publication statusPublished - 2005

Keywords

  • Hierarchical discriminant analysis
  • Texture classification

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Artificial Intelligence

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