Improving the Quality of Color Image Segmentation Using Genetic Algorithm

  • Aniceto C. AndradeJr.
  • Zenilton K. G. PatrocínioJr.
  • Silvio Jamil F. Guimarães
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8156)

Abstract

Color image segmentation is the process of grouping regions according to some criterium. In this work, we cope with this problem using a graph-based approach based on removal of minimum spanning tree edges, however the tuning of parameters is a difficult task. To better identify the set of parameters which optimizes the error producing good segmentations, we propose the use of genetic algorithm in order to establish the best set of parameters. According to test experiments, our proposed method presents better results when compared to other approaches from the literature.

Keywords

Color image segmentation genetic algorithm 

References

  1. 1.
    Bezdek, J.C.: Cluster validity with fuzzy sets. Journal of Cybernetics 3(3), 58–73 (1973), http://www.tandfonline.com/doi/abs/10.1080/01969727308546047 MathSciNetCrossRefMATHGoogle Scholar
  2. 2.
    Borsotti, M., Campadelli, P., Schettini, R.: Quantitative evaluation of color image segmentation results. Pattern Recogn. Lett. 19(8), 741–747 (1998)CrossRefMATHGoogle Scholar
  3. 3.
    Çiǧla, C., Alatan, A.: Efficient graph-based image segmentation via speeded-up turbo pixels. In: 2010 17th IEEE International Conference on Image Processing (ICIP), pp. 3013–3016 (September 2010)Google Scholar
  4. 4.
    Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient Graph-Based Image Segmentation. Int. J. Comput. Vision 59, 167–181 (2004), http://dl.acm.org/citation.cfm?id=981793.981796 CrossRefGoogle Scholar
  5. 5.
    Garcia, A., Vachier, C.: Simplification of color images using semi-flat morphological operators and statistical metrics. In: Proceedings of the 16th IEEE International Conference on Image Processing, ICIP 2009, pp. 469–472. IEEE Press, Piscataway (2009), http://dl.acm.org/citation.cfm?id=1818719.1818865 CrossRefGoogle Scholar
  6. 6.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning, 1st edn. Addison-Wesley Longman Publishing Co., Inc., Boston (1989)MATHGoogle Scholar
  7. 7.
    Grygorash, O., Zhou, Y., Jorgensen, Z.: Minimum Spanning Tree Based Clustering Algorithms. In: 18th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2006, pp. 73–81 (November 2006)Google Scholar
  8. 8.
    Guimarães, S.J.F., Cousty, J., Kenmochi, Y., Najman, L.: A hierarchical image segmentation algorithm based on an observation scale. In: Gimel’farb, G., Hancock, E., Imiya, A., Kuijper, A., Kudo, M., Omachi, S., Windeatt, T., Yamada, K. (eds.) SSPR&SPR 2012. LNCS, vol. 7626, pp. 116–125. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  9. 9.
    Holland, J.H.: Adaptation in natural and artificial systems. MIT Press, Cambridge (1992)Google Scholar
  10. 10.
    Mikolov, T.: Color Reduction Using K-Means Clustering. In: 11th Central European Seminar on Computer Graphics (April 2007)Google Scholar
  11. 11.
    Morris, O., Lee, M.J., Constantinides, A.: Graph theory for image analysis: an approach based on the shortest spanning tree. IEE Proceedings F Communications, Radar and Signal Processing 133(2), 146–152 (1986)CrossRefGoogle Scholar
  12. 12.
    Siang Tan, K., Mat Isa, N.A.: Color image segmentation using histogram thresholding - Fuzzy C-means hybrid approach. Pattern Recogn. 44(1), 1–15 (2011), http://dx.doi.org/10.1016/j.patcog.2010.07.013 CrossRefMATHGoogle Scholar
  13. 13.
    Souza, K.J.F., Guimarães, S.J.F., Patrocínio Jr., Z., Araújo, A.D.A., Cousty, J.: A Simple Hierarchical Clustering Method for Improving Flame Pixel Classification. In: 2011 23rd IEEE International Conference on Tools with Artificial Intelligence (ICTAI), pp. 110–117 (November 2011)Google Scholar
  14. 14.
    Yu, Z., Au, O.C., Zou, R., Yu, W., Tian, J.: An adaptive unsupervised approach toward pixel clustering and color image segmentation. Pattern Recogn. 43(5), 1889–1906 (2010), http://dx.doi.org/10.1016/j.patcog.2009.11.015 CrossRefMATHGoogle Scholar
  15. 15.
    Zahn, C.T.: Graph-theoretical methods for detecting and describing gestalt clusters. IEEE Trans. Comput. 20, 68–86 (1971)CrossRefMATHGoogle Scholar
  16. 16.
    Zhang, H., Fritts, J.E., Goldman, S.A.: Image segmentation evaluation: A survey of unsupervised methods. Computer Vision and Image Understanding 110(2), 260–280 (2008), http://www.sciencedirect.com/science/article/pii/S1077314207001294 CrossRefGoogle Scholar
  17. 17.
    Zhang, Y.J.: A review of recent evaluation methods for image segmentation. In: Sixth International, Symposium on Signal Processing and its Applications, vol. 1, pp. 148–151 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Aniceto C. AndradeJr.
    • 1
  • Zenilton K. G. PatrocínioJr.
    • 1
  • Silvio Jamil F. Guimarães
    • 1
  1. 1.Audio-Visual Information Proc. Lab. (VIPLAB), Computer Science DepartmentICEI – PUC MinasBrazil

Personalised recommendations