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)


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.


Genetic Programming Training Image Image Transformation Automatic Construction Linear Genetic Programming 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Cagnoni, S., Lutton, E., Olague, G. (eds.): Genetic and Evolutionary Computation for Image Processing and Analysis. EURASIP Book Series on Signal Processing and Communications, vol. 8. Hindawi Publishing Corporation (2007)Google Scholar
  2. 2.
    Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)zbMATHGoogle Scholar
  3. 3.
    Tackett, W.A.: Genetic programming for feature discovery and image discrimination. In: Proceedings of the 5th International Conference on Genetic Algorithms (ICGA 1993), pp. 303–309. Morgan Kaufmann, San Francisco (1993)Google Scholar
  4. 4.
    Teller, A., Veloso, M.: Algorithm evolution for face recognition: What makes a picture difficult. In: International Conference on Evolutionary Computation, Perth, Australia, pp. 608–613. IEEE Press, Los Alamitos (1995)Google Scholar
  5. 5.
    Teller, A., Veloso, M.: PADO: A new learning architecture for object recognition. In: Ikeuchi, K., Veloso, M. (eds.) Symbolic Visual Learning, pp. 81–116. Oxford University Press, Oxford (1996)Google Scholar
  6. 6.
    Zhang, M., Fogelberg, C.G.: Genetic programming for image recognition: An LGP approach. In: Giacobini, M. (ed.) EvoWorkshops 2007. LNCS, vol. 4448, pp. 340–350. Springer, Heidelberg (2007)Google Scholar
  7. 7.
    Lam, B., Ciesielski, V.: Discovery of human-competitive image texture feature extraction programs using genetic programming. In: Deb, K., et al. (eds.) GECCO 2004. LNCS, vol. 3103, pp. 1114–1125. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  8. 8.
    Aurnhammer, M.: Evolving texture features by genetic programming. In: Giacobini, M. (ed.) EvoWorkshops 2007. LNCS, vol. 4448, pp. 351–358. Springer, Heidelberg (2007)Google Scholar
  9. 9.
    Aoki, S., Nagao, T.: Automatic construction of tree-structural image transformation using genetic programming. In: Proceedings of the 1999 International Conference on Image Processing (ICIP 1999), Kobe, Japan, vol. 1, pp. 529–533. IEEE, Los Alamitos (1999)Google Scholar
  10. 10.
    Nakano, Y., Nagao, T.: 3D medical image processing using 3D-ACTIT; automatic construction of tree-structural image transformation. In: Proceedings of the International Workshop on Advanced Image Technology (IWAIT 2004), Singapore, pp. 529–533 (2004)Google Scholar
  11. 11.
    Nakano, Y., Nagao, T.: Automatic construction of moving object segmentation from video images using 3D-ACTIT. In: Proceedings of The 2007 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2007), Montreal, Canada, pp. 1153–1158 (2007)Google Scholar
  12. 12.
    Shirakawa, S., Nagao, T.: Genetic image network (GIN): Automatically construction of image processing algorithm. In: Proceedings of the International Workshop on Advanced Image Technology (IWAIT 2007), Bangkok, Thailand (2007)Google Scholar
  13. 13.
    Shirakawa, S., Nagao, T.: Feed forward genetic image network: Toward efficient automatic construction of image processing algorithm. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Paragios, N., Tanveer, S.-M., Ju, T., Liu, Z., Coquillart, S., Cruz-Neira, C., Müller, T., Malzbender, T. (eds.) ISVC 2007, Part II. LNCS, vol. 4842, pp. 287–297. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  14. 14.
    Miller, J.F., Thomson, P.: Cartesian genetic programming. In: Poli, R., Banzhaf, W., Langdon, W.B., Miller, J., Nordin, P., Fogarty, T.C. (eds.) EuroGP 2000. LNCS, vol. 1802, pp. 121–132. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  15. 15.
    Montana, D.J.: Strongly typed genetic programming. Evolutionary Computation 3(2), 199–230 (1995)CrossRefGoogle Scholar
  16. 16.
    Koza, J.R.: Genetic Programming II: Automatic Discovery of Reusable Programs. MIT Press, Cambridge (1994)zbMATHGoogle Scholar

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

Personalised recommendations