The Research on Image Segmentation Based on the Minimum Error Probability Bayesian Decision Theory


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The image segmentation technology has been extensively applied in many fields. As the foundation of image identification, the effective image segmentation plays a significant role during the course of subsequent image processing. Many theories and methods have been presented and discussed about image segmentation, such as K-means and fuzzy C-means methods, method based on regions information, method based on image edge detection, etc. In this work, it is proposed to apply Bayesian decision-making theory based on minimum error probability to gray image segmentation. The approach to image segmentation can guarantee the segmentation error probability minimum, which is generally what we desire. On the assumption that the gray values accord with the probability distribution of Gaussian finite mixture model in image feature space, EM algorithm is used to estimate the parameters of mixture model. In order to improve the convergence speed of EM algorithm, a novel method called weighted equal interval sampling is presented to obtain the contracted sample set. Consequently, the computation burden of EM algorithm is greatly reduced. The final experiments demonstrate the feasibility and high effectiveness of the method.



Edited by:

Dongye Sun, Wen-Pei Sung and Ran Chen






Z. Y. Chen et al., "The Research on Image Segmentation Based on the Minimum Error Probability Bayesian Decision Theory", Applied Mechanics and Materials, Vols. 121-126, pp. 1151-1155, 2012

Online since:

October 2011




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