A Gaussian Mixture Model-based clustering algorithm for image segmentation using dependable spatial constraints

In this paper, a Gaussian Mixture Model-based clustering algorithm using dependable spatial constraints is proposed for image segmentation. In order to enhance the segmentation performance, the proposed algortihm utilizes the consistence between the pixel and its local window to discriminate uncorru...

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Veröffentlicht in:2010 3rd International Congress on Image and Signal Processing Jg. 3; S. 1268 - 1272
Hauptverfasser: Weiling Cai, Lei Lei, Ming Yang
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.10.2010
Schlagworte:
Algorithm design and analysis
Clustering algorithms
clustering analysis
Gaussian Mixture Model
Image segmentation
Noise
Pixel
Robustness
Signal processing algorithms
ISBN:1424465133, 9781424465132
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Zusammenfassung:In this paper, a Gaussian Mixture Model-based clustering algorithm using dependable spatial constraints is proposed for image segmentation. In order to enhance the segmentation performance, the proposed algortihm utilizes the consistence between the pixel and its local window to discriminate uncorrupted pixels from corrupted pixels. Then, using these uncorrupted pixels, the dependable spatial constraints are applied to influence the labeling of the pixel. In this way, the spatial information with high reliability is incorporated into the segmentation process, as a result, the segmentation accuracy is guaranteed to a great extent. The extensive segmentation experiments on both synthetic and real images demonstrate the effectiveness of the proposed algorithm.
ISBN:1424465133
9781424465132
DOI:10.1109/CISP.2010.5647653