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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)

Abstract

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.

Keywords

Color image Gray level Segmentation Centroid Neural Network 

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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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