Abstract
This work presents a methodology for using genetic programming (GP) for image segmentation. The image segmentation process is seen as a classification problem where some regions of an image are labeled as foreground (object of interest) or background. GP uses a set of terminals and nonterminals, composed by algebraic operations and convolution filters. A function fitness is defined as the difference between the desired segmented image and that obtained by the application of the mask evolved by GP. A penalty term is used to decrease the number of nodes of the tree, minimally affecting the quality of solutions. The proposed approach was applied to five sets of images, each one with different features and objects of interest. Results show that GP was able to evolve solutions of high quality for the problem. Thanks to the penalty term of the fitness function, the solutions found are simple enough to be used and understood by a human user.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
References
Bhanu, B., Lin, Y.: Object detection in multimodal images using genetic programming. Appl. Soft Comput. 4(2), 175–201 (2004)
Bojarczuk, C., Lopes, H., Freitas, A., Michalkiewicz, E.: A constrained-syntax genetic programming system for discovering classification rules: application to medical data sets. Artif. Intell. Med. 30(1), 27–48 (2004)
Daves, B.: Open computer vision library. http://sourceforge.net/projects/opencvlibrary/ (2010)
Davis, J.W., Keck, M.A.: A two-stage template approach to person detection in thermal imagery. In: Proceedings of the Seventh IEEE Workshops on Application of Computer Vision, vol. 1, pp. 364–369 (2005)
Frucci, M., Baja, G.S.: From segmentation to binarization of Gray-level images. J. Pattern Recognit. Res. 3(1), 1–13 (2008)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading (1989)
Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 3rd edn. Prentice-Hall, Upper Saddle River (2006)
Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. The MIT Press, Cambridge (1992)
Kwon, S.: Threshold selection based on cluster analysis. Pattern Recognit. Lett. 25(9), 1045–1050 (2004)
Leibe, B., Leonardis, A., Schiele, B.: Robust object detection with interleaved categorization and segmentation. Int. J. Comput. Vis. 77(1–3), 259–289 (2008)
Martel-Brisson, N., Zaccarin, A.: Kernel-based learning of cast shadows from a physical model of light sources and surfaces for low-level segmentation. In: IEEE Conference on Computer Vision and, Pattern Recognition, pp. 1–8 (2008)
Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)
Punch, B., Zongker, D.: Lil-gp genetic programming system. http://garage.cse.msu.edu/software/lil-gp/ (2010)
Shapiro, L.G., Stockman, G.C.: Computer Vision. Prentice-Hall, New Jersey (2001)
Silva, R.: Erig Lima, C., Lopes, H.: Template matching in digital images using a compact genetic algorithm with elitism and mutation. J. Circuits Syst. Comput. 19(1), 91–106 (2010)
Xu, X., Xu, S., Jin, L., Song, E.: Characteristic analysis of Otsu threshold and its applications. Pattern Recognit. Lett. 32(7), 956–961 (2011)
Zhang, H., Fritts, J., Goldman, S.: Image segmentation evaluation: a survey of unsupervised methods. Comput. Vis. Image Underst. 110(2), 260–280 (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Perlin, H.A., Lopes, H.S. (2013). A Genetic Programming Approach for Image Segmentation. In: Chatterjee, A., Siarry, P. (eds) Computational Intelligence in Image Processing. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30621-1_4
Download citation
DOI: https://doi.org/10.1007/978-3-642-30621-1_4
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-30620-4
Online ISBN: 978-3-642-30621-1
eBook Packages: EngineeringEngineering (R0)