Expectation-Maximization Algorithm-Based Unsupervised Image Segmentation
L Uhai · Microcomputer Information · 2007
The paper proposes a novel image segmentation method based on Expectation-Maximization and Bayesian Information Crite-rion. The Expectation-Maximization theory is used to estimate the data distribution of the input image firstly. The number of class is calculated by Bayesian Information Criterion. The Maximum Likelihood is employed to classify the image pixels into the nearest class. The excellence of the proposed method is being independent to original estimate and it can be used in unsupervised image segmenta-tion. The synthetical and real images are used in the experiment. The results show that this method is efficient.