Low-pass Filter’s Effects on Image Analysis Using Subspace Classifier

  • Nobuo Matsuda
  • Fumiaki Tajima
  • Naoki Miyatake
  • Hideaki Sato
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 268)


This paper shows an effect for applying a low-pass filter on the performance of image analysis using the Subspace classifier. The feature extraction was firstly based on three kinds of intensity distributions, and the feature vector and subspace dimension for recognition were examined. Afterwards, a series of the analysis on the accuracies were conducted in the cases of filtered images and without filtered. The analyzed accuracies by using the Subspace classifier were also compared with the results by the technique of another: Learning vector quantization (LVQ).


Subspace Classifier Feature Space Low-pass Filter Learning Vector Quantization Fundus Image 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Tajima, F., Miyatake, N., Sato, H., Matsuda, N.: Japan Un-examined patent Kokai No. 253796 (2005)Google Scholar
  2. 2.
    Tajim, F., Chen, Y., Miyatake, N., Sato, H., Matsu-da, N.: Analysis of Eyeground Images for Diagnosis of Eyeground Diseases (1) Pseudo Three Dimensional Image of Optic Nerve Nipple Part and its Conversion to Locally Planar Inclination Image. In: 20th Fuzzy System Symposium Proceedings, p. 50 (2004) (in Japanese )Google Scholar
  3. 3.
    Kohonen, T.: Self-Organizing Maps. Springer Series in Information Sciences, vol. 30 (2001)Google Scholar
  4. 4.
    Matsuda, N., Laaksonen, J., Tajima, F., Miyatake, N., Sato, H.: Comparison with Observer Appraisals of Fundus Images and Diagnosis by using Learning Vector Quantization. In: 23th Fuzzy System Symposium Proceedings, pp. 415–418 (2007) (in Japanese)Google Scholar
  5. 5.
    Cortes, C., Vapin, V.N.: Support vector networks. Machine Learning 20, 273–295 (1995)MATHGoogle Scholar
  6. 6.
    Nishiyama, H., Hiraishi, H., Iwase, A., Mizoguch, F.: Design of Glaucoma Diagnosis System by Data Mining. In: 3A1-4 The 20th Annual Conference of the Japanese Society for Artificial Intelligence (2006) (in Japanese)Google Scholar
  7. 7.
    Watanabe, S., Pakvasa, N.: Subspace method of pattern recognition. In: 1st International Joint Conference of Pattern Recognition Proceeding, pp. 25–32 (1973)Google Scholar
  8. 8.
    Matsuda, N., Laaksonen, J., Tajima, F., Miyatake, N., Sato, H.: Fundus Image Analysis using Subspace Classifier and its Performance. In: Proceedings of the Joint 5th International Conference on Soft Computing and Intelligent Systems and 11th International Symposium on Advanced Intelligent Systems, pp. 146–151 (2010)Google Scholar
  9. 9.
    Kohonen, T., Kangas, J., Laaksonen, J., Torkkala, K.: LVQ-PAK: The Learning Vector Quantization Program Package. Helsinki University of Technology, Finland (1995)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Nobuo Matsuda
    • 1
  • Fumiaki Tajima
    • 2
  • Naoki Miyatake
    • 3
  • Hideaki Sato
    • 4
  1. 1.Dept. of Electronic and Mechanical EngineeringOshima National College of Maritime TechnologyOshima-gunJapan
  2. 2.Education and Human ScienceYokohama National UniversityHodogayaJapan
  3. 3.Chiba Institute of ScienceChibaJapan
  4. 4.Federation of National Public Service Personnel Mutual Aid AssociationTokyoJapan

Personalised recommendations