MATLAB Implementation of Mean Shift Algorithm for Image Segmentation

This MATLAB program implements the Mean Shift algorithm for image segmentation. The algorithm operates by performing clustering based on both pixel color values and spatial distribution, enabling effective image segmentation and region extraction. During program execution, the implementation initializes each pixel's spatial coordinates and color values, then iteratively computes similarity measures between pixels until convergence criteria are met. The core algorithm involves calculating multivariate kernel density estimates and shifting data points toward regions of highest density. Key MATLAB functions employed include spatial-color feature vector construction, bandwidth parameter optimization for the kernel function, and convergence detection mechanisms. Through this Mean Shift implementation, the program achieves robust image segmentation while extracting characteristic information from distinct regions, providing a fundamental framework for subsequent image processing and analysis tasks. The code structure incorporates efficient memory management for handling large images and includes visualization components for result validation.