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Topology-Preserving Dimension-Reduction Methods for Image Pattern Recognition

  • Hayato Itoh
  • Tomoya Sakai
  • Kazuhiko Kawamoto
  • Atsushi Imiya
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7944)

Abstract

In this paper, we experimentally evaluate the validity of dimension-reduction methods which preserve topology for image pattern recognition. Image pattern recognition uses pattern recognition techniques for the classification of image data. For the numerical achievement of image pattern recognition techniques, images are sampled using an array of pixels. This sampling procedure derives vectors in a higher-dimensional metric space from image patterns. For the accurate achievement of pattern recognition techniques, the dimension reduction of data vectors is an essential methodology, since the time and space complexities of data processing depend on the dimension of data. However, the dimension reduction causes information loss of geometrical and topological features of image patterns. The desired dimension-reduction method selects an appropriate low-dimensional subspace that preserves the topological information of the classification space.

Keywords

Discrete Cosine Transform Recognition Rate Image Pattern Subspace Method Random Projection 
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 2013

Authors and Affiliations

  • Hayato Itoh
    • 1
  • Tomoya Sakai
    • 2
  • Kazuhiko Kawamoto
    • 3
  • Atsushi Imiya
    • 4
  1. 1.School of Advanced Integration ScienceChiba UniversityChibaJapan
  2. 2.Graduate School of EngineeringNagasaki UniversityNagasakiJapan
  3. 3.Academic Link CenterChiba UniversityChibaJapan
  4. 4.Institute of Media and Information TechnologyChiba UniversityChibaJapan

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