Genetic Image Network for Image Classification

  • Shinichi Shirakawa
  • Shiro Nakayama
  • Tomoharu Nagao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5484)

Abstract

Automatic construction methods for image processing proposed till date approximate adequate image transformation from original images to their target images using a combination of several known image processing filters by evolutionary computation techniques. Genetic Image Network (GIN) is a recent automatic construction method for image processing. The representation of GIN is a network structure. In this paper, we propose a method of automatic construction of image classifiers based on GIN, designated as Genetic Image Network for Image Classification (GIN-IC). The representation of GIN-IC is a feed-forward network structure. GIN-IC transforms original images to easier-to-classify images using image transformation nodes, and selects adequate image features using feature extraction nodes. We apply GIN-IC to test problems involving multi-class categorization of texture images, and show that the use of image transformation nodes is effective for image classification problems.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Shinichi Shirakawa
    • 1
  • Shiro Nakayama
    • 1
  • Tomoharu Nagao
    • 1
  1. 1.Graduate School of Environment and Information SciencesYokohama National UniversityYokohamaJapan

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