Dimension Reduction Methods for Image Pattern Recognition

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• 作者：Hayato Itoh (18)
  Tomoya Sakai (19)
  Kazuhiko Kawamoto (20)
  Atsushi Imiya (21)
  
• 刊名：Lecture Notes in Computer Science
• 出版年：2013
• 出版时间：2013
• 年：2013
• 卷：7953
• 期：1
• 页码：43-58
• 全文大小：1521KB
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• 作者单位：Hayato Itoh (18)
  Tomoya Sakai (19)
  Kazuhiko Kawamoto (20)
  Atsushi Imiya (21)
  
  18. School of Advanced Integration Science, Chiba University, 1-33 Yayoicho, Inage-ku, Chiba, 263-8522, Japan
  19. Graduate School of Engineering, Nagasaki University, 1-14 Bunkyo-cho, Nagasaki, 852-8521, Japan
  20. Academic Link Center, Chiba University, 1-33 Yayoicho, Inage-ku, Chiba, 263-8522, Japan
  21. Institute of Media and Information Technology, Chiba University, 1-33 Yayoicho, Inage-ku, Chiba, 263-8522, Japan
  

文摘

In this paper, we experimentally evaluate the validity of dimension-reduction methods for the computation of the similarity in 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, 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 information used for classification.