MATLAB Implementation of Normalized Cut (Ncut) Image Segmentation Algorithm

The Normalized Cut (Ncut) image segmentation method represents a widely-used approach for partitioning images, implemented through Timothee Cour's well-structured source code. This algorithm operates by analyzing image color characteristics and pixel similarity metrics to segment images into coherent regions. The implementation typically involves constructing a weighted graph where nodes represent pixels and edges reflect similarity measures, followed by solving generalized eigenvalue problems to obtain optimal partitions. Through image segmentation, we can achieve better understanding of distinct components within images, enabling advanced analysis and processing workflows. The Ncut segmentation method finds extensive applications in computer vision and image processing domains, particularly useful for tasks such as image partitioning, object detection, and comprehensive image analysis. Timothee Cour's source code provides a robust implementation of the Ncut algorithm, offering researchers and developers a reliable tool that handles graph construction, affinity matrix computation, and spectral clustering operations efficiently. Key implementation aspects include efficient computation of similarity matrices using color and spatial features, eigenvalue decomposition for partition criteria, and recursive bipartitioning for multi-region segmentation. By utilizing this source code, users can conveniently conduct image segmentation experiments and practical applications with optimized computational performance and accurate region boundary detection. The code structure supports various image formats and allows parameter customization for different segmentation requirements.