Image Segmentation Using EM Algorithm After GMM (Gaussian Mixture Model) Fitting

After performing GMM (Gaussian Mixture Model) fitting on an image, we can utilize the EM (Expectation-Maximization) algorithm for image segmentation. GMM fitting is a statistical model-based approach that characterizes image features by modeling pixel distributions as a mixture of multiple Gaussian distributions. The EM algorithm serves as an iterative optimization method that estimates model parameters and maximizes the likelihood function. In practical implementation, the process typically involves: 1. Initializing GMM parameters (means, covariances, and mixing coefficients) using techniques like K-means clustering 2. Expectation step (E-step): Calculating posterior probabilities for each pixel belonging to different Gaussian components using Bayesian inference 3. Maximization step (M-step): Updating GMM parameters based on the computed probabilities to improve model fit 4. Iterating E and M steps until convergence criteria are met (e.g., log-likelihood stabilization) By integrating GMM fitting with the EM algorithm, we can achieve more accurate image segmentation and effectively extract features and information from different regions within the image. This approach is particularly useful for handling complex image structures where pixel distributions may not follow a single Gaussian pattern.