Wrapper Feature Construction for Figure-Ground Image Segmentation Using Genetic Programming

  • Yuyu LiangEmail author
  • Mengjie Zhang
  • Will N. Browne
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10142)


Figure-ground segmentation is a process of separating regions of interest from unimportant backgrounds. It is challenging to separate objects from target images with high variations (e.g. cluttered backgrounds), which requires effective feature sets to capture the discriminative information between object and background regions. Feature construction is a process of transforming a given set of features to a new set of high-level features, which considers the interactions between the previous features, thus the constructed features can be more meaningful and effective. As Genetic programming (GP) is a well-suited algorithm for feature construction (FC), it is employed to conduct both multiple FC (MFC) and single FC (SFC), which aims to improve the segmentation performance for the first time in this paper. The cooperative coevolution technique is introduced in GP to construct multiple features from different types of image features separately while conducting feature combination simultaneously, called as CoevoGPMFC. One wrapper method (wrapperGPSFC) is also designed, and one well-performing embedded method (embeddedGPSFC) is introduced as a reference method. Compared with the original features extracted by existing feature descriptors, the constructed features from the proposed methods are more robust and performance better on the test set. Moreover, the features constructed by the three methods achieve similar performance for the given segmentation tasks.


Figure-ground segmentation Genetic programming Feature construction Coevolution 


  1. 1.
    Borenstein, E., Ullman, S.: Combined top-down/bottom-up segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 30(12), 2109–2125 (2008)CrossRefGoogle Scholar
  2. 2.
    Cano, A., Ventura, S., Cios, K.J.: Multi-objective genetic programming for feature extraction and data visualization. Soft Comput. 1–21 (2015). doi: 10.1007/s00500-015-1907-y
  3. 3.
    Everingham, M., Eslami, S.A., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The Pascal visual object classes challenge: a retrospective. Int. J. Comput. Vis. 111(1), 98–136 (2014)CrossRefGoogle Scholar
  4. 4.
    Koza, J.R.: Genetic Programming: on the Programming of Computers by Means of Natural Selection, vol. 1. MIT Press, Cambridge (1992)zbMATHGoogle Scholar
  5. 5.
    Krawiec, K., Bhanu, B.: Coevolution and linear genetic programming for visual learning. In: Cantú-Paz, E., et al. (eds.) GECCO 2003. LNCS, vol. 2723, pp. 332–343. Springer, Heidelberg (2003). doi: 10.1007/3-540-45105-6_39 CrossRefGoogle Scholar
  6. 6.
    Kumar, M.P., Koller, D.: Efficiently selecting regions for scene understanding. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3217–3224. IEEE (2010)Google Scholar
  7. 7.
    Lee, Y.J., Grauman, K.: Object-graphs for context-aware visual category discovery. IEEE Trans. Pattern Anal. Mach. Intell. 34(2), 346–358 (2012)CrossRefGoogle Scholar
  8. 8.
    Liang, Y., Zhang, M., Browne, W.N.: Feature construction using genetic programming for figure-ground image segmentation. In: Leu, G., Singh, H.K., Elsayed, S. (eds.) Intelligent and Evolutionary Systems. PALO, vol. 8, pp. 237–250. Springer, Heidelberg (2017). doi: 10.1007/978-3-319-49049-6_17 CrossRefGoogle Scholar
  9. 9.
    Neshatian, K.: Feature manipulation with genetic programming (2010)Google Scholar
  10. 10.
    Neshatian, K., Zhang, M., Andreae, P.: A filter approach to multiple feature construction for symbolic learning classifiers using genetic programming. IEEE Trans. Evol. Comput. 16(5), 645–661 (2012)CrossRefGoogle Scholar
  11. 11.
    Poli, R.: Genetic programming for image analysis. In: Proceedings of the 1st Annual Conference on Genetic Programming, pp. 363–368. MIT Press (1996)Google Scholar
  12. 12.
    Roth, V., Lange, T.: Adaptive feature selection in image segmentation. In: Rasmussen, C.E., Bülthoff, H.H., Schölkopf, B., Giese, M.A. (eds.) DAGM 2004. LNCS, vol. 3175, pp. 9–17. Springer, Heidelberg (2004). doi: 10.1007/978-3-540-28649-3_2 CrossRefGoogle Scholar
  13. 13.
    Sondhi, P.: Feature construction methods: a survey. sifaka. cs. uiuc. edu, 69, 70–71 (2009)Google Scholar
  14. 14.
    Zou, W., Bai, C., Kpalma, K., Ronsin, J.: Online glocal transfer for automatic figure-ground segmentation. IEEE Trans. Image Process. 23(5), 2109–2121 (2014)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.School of Engineering and Computer ScienceVictoria University of WellingtonWellingtonNew Zealand

Personalised recommendations