Principal features-based texture classification with neural networks

Changjing Shang, Keith Brown

Research output: Contribution to journalLiterature review

Abstract

A texture image classification system is presented based upon the use of two cascaded multi-layer feedforward neural networks. The first network transforms a set of high-dimensional and correlated feature images into another set of dimensionally compressed, uncorrelated principal feature images whilst reducing computation effort and minimizing the information lost. The second accomplishes the task of feature pattern classification by using only those principal features obtained by the former. Such a cascaded use of neural networks significantly simplifies the structure of the classification network and increases the efficiency of the overall classification process. These are demonstrated by the comprehensive results obtained from practical applications of the system.

Original languageEnglish
Pages (from-to)675-687
Number of pages13
JournalPattern Recognition
Volume27
Issue number5
DOIs
Publication statusPublished - 1994

Keywords

  • Classification performance
  • Neural networks
  • Principal component transformation
  • Principal feature extraction
  • Texture classification

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