MATLAB Implementation for Image Recognition

This MATLAB program offers significant reference value for image recognition tasks. It implements multiple algorithms including EP (Expectation Propagation) and EM-EP (Expectation-Maximization combined with EP) approaches. Detailed technical explanations are provided below: - EP Algorithm: An efficient image recognition algorithm based on statistical principles that accurately identifies target objects in images. In MATLAB implementation, this typically involves probabilistic modeling using Gaussian distributions and message passing between connected nodes in graphical models, with key functions handling belief propagation and marginal probability calculations. - EM-EP Algorithm: An advanced image recognition approach combining the strengths of Expectation-Maximization and Expectation Propagation algorithms. This hybrid method enables more precise target localization and identification in complex images. The MATLAB code likely implements iterative parameter estimation (E-step) and optimization (M-step) cycles integrated with probabilistic inference, featuring specialized functions for handling missing data and improving convergence in challenging recognition scenarios. These detailed technical explanations and corresponding MATLAB implementations should provide substantial assistance for your image recognition projects. The code structure typically includes modular functions for data preprocessing, feature extraction, algorithm execution, and result visualization, following best practices for reproducible research.