Graph Cut Image Segmentation Algorithm Implementation

This code implements the graph cut-based image segmentation algorithm, a widely-used technique in image processing that effectively separates different objects or regions within an image. The core algorithm is implemented in C++ for optimal performance, while providing a MATLAB interface wrapper that enables seamless integration with MATLAB's image processing environment. The implementation utilizes max-flow/min-cut optimization to partition images into meaningful regions by modeling pixels as graph nodes and defining appropriate edge weights based on color similarity and spatial proximity. Key functions include graph construction with capacity constraints, energy minimization using Boykov-Kolmogorov max-flow algorithm, and boundary preservation through properly defined penalty terms. The algorithm demonstrates excellent segmentation quality, accurately distinguishing different image components while maintaining sharp and precise boundaries. Through edge-aware cost functions and adaptive regularization parameters, the implementation ensures robust performance across various image types. Users can achieve outstanding results in applications spanning medical imaging, computer vision, and photographic editing by leveraging this efficient graph-based segmentation approach.