Image Segmentation via Normalized Cuts with Implementation Details

This document presents an implementation of the Normalized Cut algorithm for image segmentation. The MATLAB code runs correctly and efficiently on MATLAB 2012b, featuring a well-structured program accompanied by relevant research paper documentation. The implementation begins by converting the input image into an affinity matrix that captures pixel similarities using Gaussian weight functions. All pixels are initialized as graph vertices in this spectral clustering framework. Through iterative partitioning computations involving eigenvalue decomposition of the graph Laplacian matrix, the algorithm progressively divides the image. The final segmentation into N subgraphs is achieved by applying threshold parameters to the computed eigenvectors, which effectively minimize the normalized cut criterion between segments while maximizing within-segment associations. The algorithm's main advantage lies in its accurate segmentation capability and flexibility for parameter adjustment and optimization based on specific application requirements. We hope this information proves valuable for your image processing projects!