1 October 1994 Color image segmentation using fuzzy clustering and supervised learning
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J. of Electronic Imaging, 3(4), (1994). doi:10.1117/12.183755
Abstract
We propose a technique for the segmentation of color map images by means of an algorithm based on fuzzy clustering and prototype optimization. Its purpose is to facilitate the extraction of lines and characters from a wide variety of geographical map images. In this method, segmentation is considered to be a process of pixel classification. The fuzzy c-means clustering algorithm is applied to a number of training areas taken from a selection of different color map images. Prototypes, generated from the clustered pixels, that satisfy a set of validation criteria are then optimized using a neural network with supervised learning. The image is segmented using the optimized prototypes according to the nearest neighbor rule. The method has been verified to work efficiently with real geographical map data.
Jing Wu, Hong Yan, Andrew N. Chalmers, "Color image segmentation using fuzzy clustering and supervised learning," Journal of Electronic Imaging 3(4), (1 October 1994). http://dx.doi.org/10.1117/12.183755
JOURNAL ARTICLE
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KEYWORDS
Image segmentation

Prototyping

Fuzzy logic

Machine learning

Image processing

Image processing algorithms and systems

Neural networks

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