An Improved ACO by Neighborhood Strategy for Color Image Segmentation

Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 274)

Abstract

This paper presents an efficient method for speeding up ant colony optimization (ACO) in solving the color image segmentation problem. The proposed method is inspired by the heuristics of image segmentation to reduce the computation time. To evaluate the performance of the proposed method, we applied the method on well-known test images. Our experimental results shows that the proposed method can significantly reduce the computation time about 19% to 45%.

Keywords

Color image segmentation clustering ant colony optimization 

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References

  1. 1.
    Haralick, R.M., Shapiro, L.G.: Image segmentation techniques. Computer Vision, Graphics, and Image Processing 29(1), 100–132 (1985)CrossRefGoogle Scholar
  2. 2.
    Yu, Z., Au, O.C., Zou, R., Yu, W., Tian, J.: An adaptive unsupervised approach toward pixel clustering and color image segmentation. Pattern Recognition 43(5), 1889–1906 (2010)Google Scholar
  3. 3.
    Tan, K.S., Isa, N.A.M., Lim, W.H.: Color image segmentation using adaptive unsupervised clustering approach. Applied Soft Computing 13(4), 2017–2036 (2013)CrossRefGoogle Scholar
  4. 4.
    Malik, J., Belongie, S., Leung, T., Shi, J.: Contour and texture analysis for image segmentation. Int. J. Comput. Vision 43(1), 7–27 (2001)MATHCrossRefGoogle Scholar
  5. 5.
    Bhanu, B., Lee, S., Ming, J.: Adaptive image segmentation using a genetic algorithm. IEEE Transactions on Systems, Man and Cybernetics 25(12), 1543–1567 (1995)Google Scholar
  6. 6.
    Bellala Belahbib, F.Z., Souami, F.: Color image segmentation by a genetic algorithm based clustering and connected component labeling. In: 2012 24th International Conference on Microelectronics (ICM), pp. 1–4 (2012)Google Scholar
  7. 7.
    Chander, A., Chatterjee, A., Siarry, P.: A new social and momentum component adaptive pso algorithm for image segmentation. Expert Systems with Applications 38(5), 4998–5004 (2011)CrossRefGoogle Scholar
  8. 8.
    Liang, Y.-C., Chen, A.H.-L., Chyu, C.-C.: Application of a hybrid ant colony optimization for the multilevel thresholding in image processing. In: King, I., Wang, J., Chan, L.-W., Wang, D. (eds.) ICONIP 2006. LNCS, vol. 4233, pp. 1183–1192. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  9. 9.
    Tao, W., Jin, H., Liu, L.: Object segmentation using ant colony optimization algorithm and fuzzy entropy. Pattern Recognition Letters 28(7), 788–796 (2007)CrossRefGoogle Scholar
  10. 10.
    Stuützle, T., Hoos, H.H.: Maxmin ant system. Future Generation Computer Systems 16(8), 889–914 (2000)CrossRefGoogle Scholar
  11. 11.
    Dorigo, M., Stuützle, T.: Ant Colony Optimization. The MIT Press (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Shih-Pang Tseng
    • 1
    • 3
  • Ming-Chao Chiang
    • 1
  • Chu-Sing Yang
    • 2
  1. 1.Department of Computer Science and EngineeringNational Sun Yat-sen UniversityKaohsiungTaiwan, R.O.C.
  2. 2.Department of Electrical EngineeringNational Cheng Kung UniversityTainanTaiwan, R.O.C.
  3. 3.Department of Computer Science and Information EngineeringTajen UniversityPingtungTaiwan, R.O.C.

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