Title :
A Gaussian Mixture Model-based clustering algorithm for image segmentation using dependable spatial constraints
Author :
Cai, Weiling ; Lei, Lei ; Yang, Ming
Author_Institution :
Dept. of Comput. Sci. & Technol., Nanjing Normal Univ., Nanjing, China
Abstract :
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.
Keywords :
Gaussian distribution; image segmentation; pattern clustering; Gaussian mixture model; clustering algorithm; dependable spatial constraints; image segmentation; Algorithm design and analysis; Clustering algorithms; Image segmentation; Noise; Pixel; Robustness; Signal processing algorithms; Gaussian Mixture Model; clustering analysis; image segmentation;
Conference_Titel :
Image and Signal Processing (CISP), 2010 3rd International Congress on
Conference_Location :
Yantai
Print_ISBN :
978-1-4244-6513-2
DOI :
10.1109/CISP.2010.5647653