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Color image segmentation using a self-initializing EM algorithm |
Ilea, Dana E.; Whelan, Paul F.
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This paper presents a new method based on the Expectation-Maximization (EM) algorithm that we apply for color image segmentation. Since this algorithm partitions the data based on an initial set of mixtures, the color segmentation provided by the EM algorithm is highly dependent on the starting condition (initialization stage). Usually the initialization procedure selects the color seeds randomly and often this procedure forces the EM algorithm to converge to numerous local minima and produce inappropriate results. In this paper we propose a simple and yet effective solution to initialize the EM algorithm with relevant color seeds. The resulting self initialised EM algorithm has been included in the development of an adaptive image segmentation scheme that has been applied to a large number of color images. The experimental data indicates that the refined initialization procedure leads to improved color segmentation.
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Keyword(s):
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Image processing; Color segmentation; Expectation-Maximization (EM); initialization; diffusion filtering |
Publication Date:
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2006 |
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Type:
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Conference item |
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Peer-Reviewed:
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Yes |
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Language(s):
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English |
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Institution:
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Dublin City University |
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Citation(s):
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Ilea, Dana E. and Whelan, Paul F. (2006) Color image segmentation using a self-initializing EM algorithm. In: VIIP 2006 - 6th International Conference on Visualization, Imaging and Image Processing, 28-30 August 2006, Palma De Mallorca, Spain. ISBN 0-88986-598-1 |
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Publisher(s):
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IASTED |
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File Format(s):
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application/pdf |
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Related Link(s):
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http://doras.dcu.ie/4676/1/DG_viip_2006.pdf |
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First Indexed:
2009-11-05 02:01:35 Last Updated:
2012-05-21 05:13:05 |
