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Evolving semantic object segmentation methods automatically by genetic programming from images and image processing operators

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Even though numerous segmentation methods exist, the requirement of prior knowledge or parameter tuning makes them restricted to limited image domains. Without predefining solution models, genetic programming (GP) is able to solve complex problems by evolving computer programs automatically. In this paper, three new GP-based methods are designed to evolve segmentation algorithms automatically from images and primitive image processing operators (e.g., filters and histogram equalization). Specifically, a strongly typed representation, the cooperative coevolution technique and a two-stage evolution are introduced in GP, respectively, to form three new methods that can evolve solutions to conduct image preprocessing, segmentation and postprocessing automatically. The new methods are termed as StronglyGP, CoevoGP and TwostageGP, and standard GP-based algorithm (StandardGP) is employed as a reference method. The proposed methods are tested on two complicated datasets (i.e., Weizmann and Pascal datasets), which contain high variations in both objects and backgrounds. The results show that StronglyGP and StandardGP can evolve effective segmentors for the given complex segmentation tasks, while CoevoGP and TwostageGP perform worse than StronglyGP and StandardGP, which may be caused by the overfitting problem in deriving postprocessing solutions. In addition, compared with StandardGP, StronglyGP achieves better segmentation performance with smaller solution sizes. Moreover, compared with four widely used segmentation methods, StronglyGP and StandardGP can produce satisfactory results consistently on both Weizmann and Pascal datasets.

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    Bloat is known as a problem that “programs grow without the corresponding increase in the fitness” (Poli and Mcphee 2014).


  1. Active contour based segmentation. http://au.mathworks.com/help/images/ref/activecont-our.htmlbtuep4x-7

  2. Thresholding segmentation. http://au.mathworks.com/help/images/examples/correcting-nonuniform-illumination.html

  3. Banzhaf W, Nordin P, Keller RE, Francone FD (1998) Genetic programming: an introduction. Morgan Kaufmann Publishers, San Francisco

  4. Borenstein E, Ullman S (2008) Combined top-down/bottom-up segmentation. IEEE Trans Pattern Anal Mach Intell 30(12):2109–2125

  5. Dong M, Eramian MG, Ludwig SA, Pierson RA (2013) Automatic detection and segmentation of bovine corpora lutea in ultrasonographic ovarian images using genetic programming and rotation invariant local binary patterns. Med Biol Eng Comput 51(4):405–416

  6. Espejo PG, Ventura S, Herrera F (2010) A survey on the application of genetic programming to classification. IEEE Trans Syst Man Cybern Part C 40(2):121–144

  7. Everingham M, Eslami SA, Van Gool L, Williams CK, Winn J, Zisserman A (2014) The pascal visual object classes challenge: a retrospective. Int J Comput Vis 111(1):98–136

  8. Fonseca P K-means image segmentation. http://www.mathworks.com/matlabcentral/fileexchange/authors/129300

  9. Fu KS, Mui J (1981) A survey on image segmentation. Pattern Recogn 13(1):3–16

  10. Ghosh P, Mitchell M (2006) Segmentation of medical images using a genetic algorithm. In: Proceedings of the 8th annual conference on genetic and evolutionary computation. ACM, pp 1171–1178

  11. Gill G, Toews M, Beichel RR (2014) Robust initialization of active shape models for lung segmentation in ct scans: a feature-based atlas approach. J Biomed Imaging 2014:13

  12. Han D (2013) Comparison of commonly used image interpolation methods. ICCSEE, Hangzhou, pp 1556–1559

  13. Koza J (1992) Genetic programming: on the programming of computers by natural selection. MIT Press, Cambridge

  14. Kroon D (2008) Region growing. http://www.mathworks.com/matlabcentral/fileexchange/19084-region-growing

  15. Liang Y, Zhang M, Browne WN (2017) Learning figure-ground image segmentors by genetic programming. In: The genetic and evolutionary computation conference. ACM, pp 239–240

  16. Liu T, Xu H, Jin W, Liu Z, Zhao Y, Tian W (2014) Medical image segmentation based on a hybrid region-based active contour model. Comput Math Methods Med 2014:890725

  17. Luke S (2010) The ECJ owner’s manual. A user manual for the ECJ evolutionary computation library. ECJ, San Francisco, pp 1–206

  18. Luke S, Panait L, Balan G, Paus S, Skolicki Z, Bassett J, Hubley R, Chircop A (2006) ECJ: A java-based evolutionary computation research system. Downloadable versions and documentation can be found at the following http://cs.gmu.edu/eclab/projects/ecj

  19. McKnight PE, Najab J (2010) Mann-Whitney U test. Corsini Encyclopedia of Psychology, London

  20. Pal NR, Pal SK (1993) A review on image segmentation techniques. Pattern Recogn 26(9):1277–1294

  21. Poli R (1996) Genetic programming for image analysis. In: Proceedings of the 1st annual conference on genetic programming. MIT Press, pp 363–368

  22. Poli R, Langdon WB, McPhee NF, Koza JR (2008) A field guide to genetic programming. Lulu.com

  23. Poli R, Mcphee NF (2014) Parsimony pressure made easy: solving the problem of bloat in GP. In: Theory and principled methods for the design of metaheuristics. Natural Computing Series. Springer, Berlin, Heidelberg

  24. Roberts ME (2003) The effectiveness of cost based subtree caching mechanisms in typed genetic programming for image segmentation. In: Workshops on applications of evolutionary computation. Springer, pp 444–454

  25. Sasaki Y (2007) The truth of the F-measure. Teach Tutor Mater 1:1–5

  26. Singh T, Kharma N, Daoud M, Ward R (2009) Genetic programming based image segmentation with applications to biomedical object detection. In: Proceedings of the 11th annual conference on genetic and evolutionary computation. ACM, pp 1123–1130

  27. Song A, Ciesielski V (2003) Fast texture segmentation using genetic programming. In: The 2003 congress on evolutionary computation, 2003. CEC’03, vol 3. IEEE, pp 2126–2133

  28. Song A, Ciesielski V (2008) Texture segmentation by genetic programming. Evol Comput 16(4):461–481

  29. Wang X, You S, Li X, Ma H (2018) Weakly-supervised semantic segmentation by iteratively mining common object features. In: 2018 IEEE/CVF conference on computer vision and pattern recognition (CVPR)

  30. Winkeler JF, Manjunath B (1997) Genetic programming for object detection. Genet Progr 25:330–335

  31. Zou W, Bai C, Kpalma K, Ronsin J (2014) Online glocal transfer for automatic figure-ground segmentation. IEEE Trans Image Process 23(5):2109–2121

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This work is supported by National Natural Science Foundation of China with Grant No. 61902281 and Tianjin Science and Technology Program with Grant No. 19PTZ-WHZ00020.

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Correspondence to Jianming Wang.

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JL declares that she has no conflict of interest. JW declares that he has no conflict of interest. ZW declares that he has no conflict of interest. JW declares that he has no conflict of interest.

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Liang, J., Wen, J., Wang, Z. et al. Evolving semantic object segmentation methods automatically by genetic programming from images and image processing operators. Soft Comput (2020). https://doi.org/10.1007/s00500-020-04713-1

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  • Semantic object segmentation
  • Genetic programming
  • Cooperative coevolution
  • Strongly typed representation