MATLAB Implementation of Ncut Segmentation Algorithm

The Ncut (Normalized Cut) segmentation algorithm is a powerful method in image processing used to achieve high-quality image segmentation. This implementation utilizes MATLAB code to partition images effectively, enabling the extraction of distinct features and content from different regions within the image. The algorithm operates by constructing a weighted graph representation of the image where pixels represent nodes and similarity measures define edge weights. Key implementation steps include computing the affinity matrix, solving generalized eigenvalue problems, and performing recursive bipartitioning to separate image regions based on their spectral properties. This method has gained widespread application in the image processing domain and has demonstrated excellent performance in various segmentation tasks. The MATLAB implementation typically involves functions for computing pixel similarities, graph Laplacian matrices, and eigenvectors for spectral clustering. Therefore, employing the Ncut segmentation algorithm for image segmentation proves to be an efficient and reliable approach, particularly useful for applications requiring precise region separation and feature extraction.