A One-Shot Learning Approach to Image Classification Using Genetic Programming

  • Harith Al-Sahaf
  • Mengjie Zhang
  • Mark Johnston
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8272)


In machine learning, it is common to require a large number of instances to train a model for classification. In many cases, it is hard or expensive to acquire a large number of instances. In this paper, we propose a novel genetic programming (GP) based method to the problem of automatic image classification via adopting a one-shot learning approach. The proposed method relies on the combination of GP and Local Binary Patterns (LBP) techniques to detect a predefined number of informative regions that aim at maximising the between-class scatter and minimising the within-class scatter. Moreover, the proposed method uses only two instances of each class to evolve a classifier. To test the effectiveness of the proposed method, four different texture data sets are used and the performance is compared against two other GP-based methods namely Conventional GP and Two-tier GP. The experiments revealed that the proposed method outperforms these two methods on all the data sets. Moreover, a better performance has been achieved by Naïve Bayes, Support Vector Machine, and Decision Trees (J48) methods when extracted features by the proposed method have been used compared to the use of domain-specific and Two-tier GP extracted features.


Genetic Programming Local Binary Patterns Image Classification One-shot Learning 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Ahonen, T., Hadid, A., Pietikainen, M.: Face description with local binary patterns: Application to face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(12), 2037–2041 (2006)CrossRefGoogle Scholar
  2. 2.
    Al-Sahaf, H., Song, A., Neshatian, K., Zhang, M.: Extracting image features for classification by two-tier genetic programming. In: IEEE Congress on Evolutionary Computation, pp. 1–8. IEEE (2012)Google Scholar
  3. 3.
    Biederman, I.: Recognition-by-components: A theory of human image understanding. Psychological Review 94, 115–147 (1987)CrossRefGoogle Scholar
  4. 4.
    Dana, K.J., van Ginneken, B., Nayar, S.K., Koenderink, J.J.: Reflectance and texture of real-world surfaces. ACM Transactions on Graphics 18(1), 1–34 (1999)CrossRefGoogle Scholar
  5. 5.
    Duin, R.P.: Small sample size generalization. In: Proceedings of the Ninth Scandinavian Conference on Image Analysis, Uppsala, Sweden, vol. 2, pp. 957–964 (1995)Google Scholar
  6. 6.
    Fei-Fei, L., Fergus, R., Perona, P.: One-shot learning of object categories. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(4), 594–611 (2006)CrossRefGoogle Scholar
  7. 7.
    Fergus, R., Perona, P., Zisserman, A.: Object class recognition by unsupervised scale-invariant learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 264–271 (June 2003)Google Scholar
  8. 8.
    Hegenbart, S., Maimone, S., Uhl, A., Vécsei, A., Wimmer, G.: Customised frequency pre-filtering in a local binary pattern-based classification of gastrointestinal images. In: Greenspan, H., Müller, H., Syeda-Mahmood, T. (eds.) MCBR-CDS 2012. LNCS, vol. 7723, pp. 99–109. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  9. 9.
    Jain, A.K., Chandrasekaran, B.: Dimensionality and sample size considerations in pattern recognition practice. In: Classification Pattern Recognition and Reduction of Dimensionality, vol. 2, pp. 835–855. Elsevier (1982)Google Scholar
  10. 10.
    Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)zbMATHGoogle Scholar
  11. 11.
    Kylberg, G.: The Kylberg texture dataset v. 1.0. External report (Blue series) 35, Centre for Image Analysis, Swedish University of Agricultural Sciences and Uppsala University, Uppsala, Sweden (2011)Google Scholar
  12. 12.
    Lake, B.M., Salakhutdinov, R., Gross, J., Tenenbaum, J.B.: One shot learning of simple visual concepts. In: Proceedings of the 33rd Annual Conference of the Cognitive Science Society, Austin, TX, pp. 2568–2573 (2011)Google Scholar
  13. 13.
    Liu, L., Zhao, L., Long, Y., Kuang, G., Fieguth, P.: Extended local binary patterns for texture classification. Image and Vision Computing 30(2), 86–99 (2012)CrossRefGoogle Scholar
  14. 14.
    Luke, S.: Essentials of Metaheuristics, 2nd edn. Lulu (2013),
  15. 15.
    Montana, D.J.: Strongly typed genetic programming. Evolutionary Computation 3(2), 199–230 (1995)CrossRefGoogle Scholar
  16. 16.
    Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on feature distributions. Pattern Recognition 29(1), 51–59 (1996)CrossRefGoogle Scholar
  17. 17.
    Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(7), 971–987 (2002)CrossRefGoogle Scholar
  18. 18.
    Pietikäinen, M., Hadid, A., Zhao, G., Ahonen, T.: Local binary patterns for still images. In: Computer Vision Using Local Binary Patterns. Computational Imaging and Vision, vol. 40, pp. 13–47. Springer London (2011)Google Scholar
  19. 19.
    Porter, F.C.: Testing Consistency of Two Histograms. ArXiv e-prints, pp. 1–35 (2008)Google Scholar
  20. 20.
    Raudys, S.J., Jain, A.K.: Small sample size effects in statistical pattern recognition: Recommendations for practitioners. IEEE Transactions on Pattern Analysis and Machine Intelligence 13(3), 252–264 (1991)CrossRefGoogle Scholar
  21. 21.
    Salakhutdinov, R., Tenenbaum, J.B., Torralba, A.: One-shot learning with a hierarchical nonparametric bayesian model. Journal of Machine Learning Research - Proceedings Track 27, 195–206 (2012)Google Scholar
  22. 22.
    Schneiderman, H., Kanade, T.: A statistical method for 3d object detection applied to faces and cars. In: Proceedings of Computer Vision and Pattern Recognition, pp. 1746–1759. IEEE Computer Society (2000)Google Scholar
  23. 23.
    Viola, P.A., Jones, M.J.: Rapid object detection using a boosted cascade of simple features. In: Proceeding of Computer Vision and Pattern Recognition, pp. 511–518. IEEE Computer Society (2001)Google Scholar
  24. 24.
    Weber, M., Welling, M., Perona, P.: Unsupervised learning of models for recognition. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1842, pp. 18–32. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  25. 25.
    Xie, J., Zhang, D., You, J., Zhang, D.: Texture classification via patch-based sparse texton learning. In: IEEE International Conference on Image Processing (ICIP), pp. 2737–2740 (2010)Google Scholar
  26. 26.
    Yip, K., Sussman, G.J.: Sparse representations for fast, one-shot learning. In: Proceedings of the Fourteenth National Conference on Artificial Intelligence, pp. 521–527. AAAI Press / The MIT Press (1997)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Harith Al-Sahaf
    • 1
  • Mengjie Zhang
    • 1
  • Mark Johnston
    • 1
  1. 1.Evolutionary Computation Research GroupVictoria University of WellingtonWellingtonNew Zealand

Personalised recommendations