MATLAB Implementation of K-Medoids Clustering for Image Features

This is a MATLAB implementation of K-medoids clustering for image features. K-medoids clustering is a widely-used clustering algorithm that partitions data points into K clusters, with each cluster represented by a central point (medoid). This algorithm has extensive applications in image processing and pattern recognition, including image classification, object detection, and other computer vision tasks. In this MATLAB implementation, we employ the K-medoids algorithm to identify the optimal medoids for each cluster and assign data points to their respective clusters based on these central points. The implementation typically involves several key steps: 1. Initialization: Randomly selecting K initial medoids from the dataset 2. Assignment: Calculating distances between each data point and all medoids using appropriate distance metrics (Euclidean, Manhattan, etc.) 3. Update: Recomputing medoids by selecting the data point that minimizes the total distance to all other points in the cluster 4. Iteration: Repeating the assignment and update steps until convergence Key MATLAB functions utilized in this implementation may include pdist2 for distance calculations, kmedoids built-in function (if using Statistics and Machine Learning Toolbox), or custom implementations of the Partitioning Around Medoids (PAM) algorithm. The code handles feature vector organization, distance matrix computation, and cluster assignment optimization. This implementation provides a convenient tool for conducting cluster analysis on image features, enabling researchers and developers to perform subsequent image processing tasks such as feature quantization, image segmentation, and pattern recognition with improved efficiency and accuracy. The modular design allows for customization of distance metrics and initialization methods to suit specific image feature characteristics.