Genetic Programming for Region Detection, Feature Extraction, Feature Construction and Classification in Image Data

  • Andrew Lensen
  • Harith Al-SahafEmail author
  • Mengjie Zhang
  • Bing Xue
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9594)


Image analysis is a key area in the computer vision domain that has many applications. Genetic Programming (GP) has been successfully applied to this area extensively, with promising results. High-level features extracted from methods such as Speeded Up Robust Features (SURF) and Histogram of Oriented Gradients (HoG) are commonly used for object detection with machine learning techniques. However, GP techniques are not often used with these methods, despite being applied extensively to image analysis problems. Combining the training process of GP with the powerful features extracted by SURF or HoG has the potential to improve the performance by generating high-level, domain-tailored features. This paper proposes a new GP method that automatically detects different regions of an image, extracts HoG features from those regions, and simultaneously evolves a classifier for image classification. By extending an existing GP region selection approach to incorporate the HoG algorithm, we present a novel way of using high-level features with GP for image classification. The ability of GP to explore a large search space in an efficient manner allows all stages of the new method to be optimised simultaneously, unlike in existing approaches. The new approach is applied across a range of datasets, with promising results when compared to a variety of well-known machine learning techniques. Some high-performing GP individuals are analysed to give insight into how GP can effectively be used with high-level features for image classification.


Genetic programming Image classification Feature extraction Feature construction 


  1. 1.
    Agarwal, S., Awan, A., Roth, D.: Learning to detect objects in images via a sparse, part-based representation. IEEE Trans. Pattern Anal. Mach. Intell. 26(11), 1475–1490 (2004)CrossRefGoogle Scholar
  2. 2.
    Al-Sahaf, H., Song, A., Neshatian, K., Zhang, M.: Two-tier genetic programming: towards raw pixel-based image classification. Expert Syst. Appl. 39(16), 12291–12301 (2012)CrossRefGoogle Scholar
  3. 3.
    Al-Sahaf, H., Zhang, M., Johnston, M.: Genetic programming evolved filters from a small number of instances for multiclass texture classification. In: Proceedings of the 29th International Conference on Image and Vision Computing New Zealand, pp. 84–89. ACM (2014)Google Scholar
  4. 4.
    Al-Sahaf, H., Zhang, M., Johnston, M.: Binary image classification: a genetic programming approach to the problem of limited training instances. Evolutionary Computation (Journal, MIT Press) (2015). doi: 10.1162/EVCO_a_00146 Google Scholar
  5. 5.
    Al-Sahaf, H., Zhang, M., Johnston, M., Verma, B.: Image descriptor: a genetic programming approach to multiclass texture classification. In: Proceedings of 2015 IEEE Congress on Evolutionary Computation, pp. 2460–2467. IEEE (2015)Google Scholar
  6. 6.
    Back, T., Fogel, D.B., Michalewicz, Z.: Handbook of Evolutionary Computation. IOP Publishing Ltd., Bristol (1997)CrossRefzbMATHGoogle Scholar
  7. 7.
    Cheng, F., Yu, J., Xiong, H.: Facial expression recognition in JAFFE dataset based on gaussian process classification. IEEE Trans. Neural Netw. 21(10), 1685–1690 (2010)CrossRefGoogle Scholar
  8. 8.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 886–893. IEEE (2005)Google Scholar
  9. 9.
    Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. Springer Science & Business Media, Heidelberg (2003)CrossRefzbMATHGoogle Scholar
  10. 10.
    Espejo, P.G., Ventura, S., Herrera, F.: A survey on the application of genetic programming to classification. IEEE Trans. Syst. Man Cybern. Part C: Appl. Rev. 40(2), 121–144 (2010)CrossRefGoogle Scholar
  11. 11.
    Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. SIGKDD Explor. Newsl. 11(1), 10–18 (2009)CrossRefGoogle Scholar
  12. 12.
    Huang, Y., Wu, Z., Wang, L., Tan, T.: Feature coding in image classification: a comprehensive study. IEEE Trans. Pattern Anal. Mach. Intell. 36(3), 493–506 (2014)CrossRefGoogle Scholar
  13. 13.
    Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)zbMATHGoogle Scholar
  14. 14.
    Krawiec, K.: Genetic programming-based construction of features for machine learning and knowledge discovery tasks. Genet. Program. Evolvable Mach. 3(4), 329–343 (2002)CrossRefzbMATHGoogle Scholar
  15. 15.
    Lowe, D.G.: Object recognition from local scale-invariant features. In: Proceedings of the International Conference on Computer Vision, pp. 1150–1157. IEEE (1999)Google Scholar
  16. 16.
    Lyons, M., Akamatsu, S., Kamachi, M., Gyoba, J.: Coding facial expressions with gabor wavelets. In: Proceedings of the 3rd International Conference on Face & Gesture Recognition, pp. 200–205. IEEE (1998)Google Scholar
  17. 17.
    Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 27(10), 1615–1630 (2005)CrossRefGoogle Scholar
  18. 18.
    Montana, D.J.: Strongly typed genetic programming. Evol. Comput. 3(2), 199–230 (1995)CrossRefGoogle Scholar
  19. 19.
    Nene, S.A., Nayar, S.K., Murase, H.: Columbia Object Image Library (COIL-20). Technical report (1996)Google Scholar
  20. 20.
    Perez, C.B., Olague, G.: Evolutionary learning of local descriptor operators for object recognition. In: Proceedings of the 11th Annual conference on Genetic and evolutionary computation, pp. 1051–1058. ACM (2009)Google Scholar
  21. 21.
    Poli, R.: Genetic programming for feature detection and image segmentation. In: Fogarty, T.C. (ed.) AISB-WS 1996. LNCS, vol. 1143, pp. 110–125. Springer, Heidelberg (1996)CrossRefGoogle Scholar
  22. 22.
    Saini, R., Dutta, M.: Image segmentation for uneven lighting images using adaptive thresholding and dynamic window based on incremental window growing approach. Int. J. Comput. Appl. 56(13), 31–36 (2012)Google Scholar
  23. 23.
    Shao, L., Liu, L., Li, X.: Feature learning for image classification via multiobjective genetic programming. IEEE Trans. Neural Netw. Learn. Syst. 25(7), 1359–1371 (2014)CrossRefGoogle Scholar
  24. 24.
    Winkeler, J.F., Manjunath, B.: Genetic programming for object detection. In: Proceedings of the Second Annual Conference on Genetic Programming, pp. 330–335. Morgan Kaufmann (1997)Google Scholar
  25. 25.
    Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)zbMATHGoogle Scholar
  26. 26.
    Zhang, M., Ciesielski, V., Andreae, P.: A domain-independent window approach to multiclass object detection using genetic programming. EURASIP J. Adv. Signal Process. 2003(8), 841–859 (2003)CrossRefzbMATHGoogle Scholar
  27. 27.
    Zhao, Y., Zhang, Y., Cheng, R., Wei, D., Li, G.: An enhanced histogram of oriented gradients for pedestrian detection. IEEE Intell. Transp. Syst. Mag. 7(3), 29–38 (2015)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Andrew Lensen
    • 1
  • Harith Al-Sahaf
    • 1
    Email author
  • Mengjie Zhang
    • 1
  • Bing Xue
    • 1
  1. 1.School of Engineering and Computer ScienceVictoria University of WellingtonWellingtonNew Zealand

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