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.
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Sang, DT., Woo, DM., Park, DC. (2012). Centroid Neural Network with Simulated Annealing and Its Application to Color Image Segmentation. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7665. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34487-9_1
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DOI: https://doi.org/10.1007/978-3-642-34487-9_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34486-2
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