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 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.