Centroid neural network with simulated annealing and its application to color image segmentation

Authors:
Do-Thanh Sang;Dong-Min Woo;Dong-Chul Park
Affiliations:
Dept. of Electronics Engineering, Myongji University, Korea;Dept. of Electronics Engineering, Myongji University, Korea;Dept. of Electronics Engineering, Myongji University, Korea
Venue:
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
Year:
2012

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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.