K-Means Clustering Algorithm Implementation in MATLAB

Application Background

This k-means image segmentation and clustering algorithm is designed to partition images into distinct regions and perform cluster analysis. The algorithm finds extensive applications in image processing and computer vision domains, holding significant practical value. By leveraging MATLAB's computational capabilities, we can efficiently implement this clustering technique with functions like kmeans() for centroid calculation and pdist2() for distance measurement between pixels and cluster centers.

The k-means algorithm operates through an iterative process: initializing cluster centroids, assigning pixels to nearest clusters based on color/intensity features, and recalculating centroids until convergence. This MATLAB implementation specifically handles 3-cluster segmentation, where image pixels are classified into three groups using Euclidean distance metrics. The algorithm effectively groups similar pixels into coherent regions, enabling researchers to analyze regional characteristics and properties through cluster visualization and statistical analysis of segment features.