CRF-based Image Segmentation Implementation

This article explores the implementation of image segmentation using Conditional Random Fields (CRF). To facilitate better understanding of the process, we provide MATLAB code examples demonstrating the CRF framework. The implementation involves constructing energy functions that combine unary potentials (pixel-wise classifications) and pairwise potentials (spatial relationships between neighboring pixels). Key functions include graph construction using neighboring pixel connections and energy minimization through inference algorithms like max-flow/min-cut. It's important to note that the source code requires external dataset integration, as training/testing images need to be downloaded separately for proper execution. Through this tutorial, you'll gain comprehensive insights into CRF applications in image segmentation and learn practical MATLAB implementation techniques for probabilistic graphical models, including parameter learning and inference optimization methods.