Centroid Neural Network with Simulated Annealing and Its Application to Color Image Segmentation

  • Do-Thanh Sang
  • Dong-Min Woo
  • Dong-Chul Park
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7665)


Centroid Neural Network (CNN) with simulated annealing is proposed and applied to a color image segmentation problem in this paper. CNN is essentially an unsupervised competitive neural network scheme and is a crucial algorithm to diminish the empirical process of parameter adjustment required in many unsupervised competitive learning algorithms including Self-Organizing Map. In order to achieve lower energy level during its training stage further, a supervised learning concept, called simulated annealing, is adopted. As a result, the final energy level of CNN with simulated annealing (CNN-SA) can be much lower than that of the original Centroid Neural Network. The proposed CNN-SA algorithm is applied to a color image segmentation problem. The experimental results show that the proposed CNN-SA can yield favorable segmentation results when compared with other conventional algorithms.


Color image Gray level Segmentation Centroid Neural Network 


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  1. 1.
    Huang, L.K., Wang, M.J.: Image Thresholding by Minimizing the Measures of Fuzziness. Pattern Recogn. 28, 41–51 (1995)CrossRefGoogle Scholar
  2. 2.
    Yang, J.F., Hao, S.S., Chung, P.C.: Color Image Segmentation Using Fuzzy C-Means and Eigenspace Projections. Signal Process. 82, 461–472 (2002)zbMATHCrossRefGoogle Scholar
  3. 3.
    Kohonen, T.: Self-Organizing Maps. Springer, Berlin (1995)CrossRefGoogle Scholar
  4. 4.
    Dong, G., Xie, M.: Color Clustering and Learning for Image Segmentation Based on Neural Networks. IEEE Trans. Neural Networks 16, 925–936 (2005)CrossRefGoogle Scholar
  5. 5.
    Park, D.C.: Centroid Neural Network for Unsupervised Competitive Learning. IEEE Trans. Neural Networks 11, 520–528 (2000)CrossRefGoogle Scholar
  6. 6.
    Cheng, H.D., Jiang, X.H., Sun, Y., Wang, J.: Color Image Segmentation: Advances and Prospects. Pattern Recogn. 34, 2259–2281 (2001)zbMATHCrossRefGoogle Scholar
  7. 7.
    Borsotti, M., Campadelli, P., Schettini, R.: Quantitiative Evaluation of Color Image Segmentation Results. Pattern Recogn. Lett. 19, 741–747 (1998)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Do-Thanh Sang
    • 1
  • Dong-Min Woo
    • 1
  • Dong-Chul Park
    • 1
  1. 1.Dept. of Electronics EngineeringMyongji UniversityKorea

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