A multi-manifold discriminant analysis method for image feature extraction

Wankou Yang, Changyin Sun, Lei Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

162 Citations (Scopus)

Abstract

In this paper, we propose a Multi-Manifold Discriminant Analysis (MMDA) method for an image feature extraction and pattern recognition based on graph embedded learning and under the Fisher discriminant analysis framework. In an MMDA, the within-class graph and between-class graph are, respectively, designed to characterize the within-class compactness and the between-class separability, seeking for the discriminant matrix to simultaneously maximize the between-class scatter and minimize the within-class scatter. In addition, in an MMDA, the within-class graph can represent the sub-manifold information, while the between-class graph can represent the multi-manifold information. The proposed MMDA is extensively examined by using the FERET, AR and ORL face databases, and the PolyU finger-knuckle-print databases. The experimental results demonstrate that an MMDA is effective in feature extraction, leading to promising image recognition performance.
Original languageEnglish
Pages (from-to)1649-1657
Number of pages9
JournalPattern Recognition
Volume44
Issue number8
DOIs
Publication statusPublished - 1 Aug 2011

Keywords

  • Feature extraction
  • Image recognition
  • LDA
  • Multi-manifold learning

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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