Quantitative evaluation of color image segmentation results
Pattern Recognition Letters
Self-Organizing Maps
Color image segmentation using fuzzy C-means and eigenspace projections
Signal Processing
Centroid neural network for unsupervised competitive learning
IEEE Transactions on Neural Networks
Color clustering and learning for image segmentation based on neural networks
IEEE Transactions on Neural Networks
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Color image segmentation has been attracting more and more attention, mainly because color images can provide more information than gray level images. Many methods have been proposed so far to deal with the problem. However, most methods require fine tuning of parameters, which can be attained after repetitive trial and error. This paper discusses unsupervised learning in terms of Centroid Neural Network (CNN). In fact, CNN is the crucial algorithm to diminish the empirical process of parameter adjustment required for color image segmentation. The simulation results indicate that the proposed technique yields the reasonably segmented images in comparison with other conventional algorithms.