Classic Graph Cut Algorithm: Normalized Cuts for Image Segmentation

In the field of image segmentation, the classic Normalized Cuts algorithm stands as a fundamental approach that applies graph theory concepts to partition images effectively. The algorithm works by representing the image as a weighted graph where pixels correspond to nodes and edge weights represent similarity between pixels. The normalized cut criterion minimizes the similarity between different segments while maximizing similarity within each segment. To implement and test this algorithm, execute the demo file demoNcutImage.m, which demonstrates the segmentation process and visual results. The implementation includes configurable parameters where you can modify the input image filename and specify the desired number of segmentation blocks to accommodate various requirements. The MATLAB implementation typically involves constructing the affinity matrix, solving the generalized eigenvalue problem, and applying clustering techniques to the eigenvectors for final segmentation.