Ensemble Image Classification Method Based on Genetic Image Network

  • Shiro Nakayama
  • Shinichi Shirakawa
  • Noriko Yata
  • Tomoharu Nagao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6021)

Abstract

Automatic construction method for image classification algorithms have been required. Genetic Image Network for Image Classification (GIN-IC) is one of the methods that construct image classification algorithms automatically, and its effectiveness has already been proven. In our study, we try to improve the performance of GIN-IC with AdaBoost algorithm using GIN-IC as weak classifiers to complement with each other. We apply our proposed method to three types of image classification problems, and show the results in this paper. In our method, discrimination rates for training images and test images improved in the experiments compared with the previous method GIN-IC.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Shirakawa, S., Nakayama, S., Nagao, T.: Genetic Image Network for Image Classification. In: Giacobini, M., Brabazon, A., Cagnoni, S., Di Caro, G.A., Ekárt, A., Esparcia-Alcázar, A.I., Farooq, M., Fink, A., Machado, P. (eds.) EvoCOMNET. LNCS, vol. 5484, pp. 395–404. Springer, Heidelberg (2009)Google Scholar
  2. 2.
    Freund, Y., Schapire, R.E.: Experiments with a New Boosting Algorithm. In: Proceedings of the 13th International Conference on Machine Leaning (ICML 1996), pp. 148–156 (1996)Google Scholar
  3. 3.
    Viola, P., Jones, M.: Rapid Object Detection using a Boosted Cascade of Simple Features. In: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR 2001), vol. 1, pp. 511–518 (2001)Google Scholar
  4. 4.
    Iba, H.: Bagging, Boosting, and Bloating in Genetic Programming. In: Proceedings of the Genetic and Evolutionary Computation Conference 1999 (GECCO 1999), vol. 2, pp. 1053–1060 (1999)Google Scholar
  5. 5.
    Folino, G., Pizzuti, C., Spezzano, G.: Boosting Technique for Combining Cellular GP Classifiers. In: Keijzer, M., O’Reilly, U.-M., Lucas, S., Costa, E., Soule, T. (eds.) EuroGP 2004. LNCS, vol. 3003, pp. 47–56. Springer, Heidelberg (2004)Google Scholar
  6. 6.
    Folino, G., Pizzuti, C., Spezzano, G.: GP Ensembles for Large-scale Data Classification. IEEE Transaction on Evolutionary Computation 10(5), 604–616 (2006)CrossRefGoogle Scholar
  7. 7.
    Mohemmed, A.W., Zhang, M., Johnston, M.: Particle Swarm Optimization Based Adaboost for Face Detection. In: Proceedings of the 2009 IEEE Congress on Evolutionary Computation, pp. 2494–2501. IEEE Press, Los Alamitos (2009)CrossRefGoogle Scholar
  8. 8.
    Schwenk, H., Bengio, Y.: Boosting Neural Networks. Neural Computation 12(8), 1869–1887 (2000)CrossRefGoogle Scholar
  9. 9.
    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
  10. 10.
    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)Google Scholar
  11. 11.
    Lowe, D.G.: Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision (IJCV) 60(2), 91–110 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

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

Personalised recommendations