Image Classification and Segmentation Using K-means Clustering

Image classification and segmentation processing using k-means clustering in MATLAB. K-means clustering is a widely used image processing technique that partitions image pixels into different categories based on similarity, enabling effective image classification and segmentation. This method involves selecting an appropriate k value to divide image pixels into k clusters, then determining each pixel's category based on the centroid values of each cluster.

In MATLAB implementation, the k-means algorithm typically utilizes the kmeans() function, which requires input parameters including the image data matrix, number of clusters (k), and distance metric options. The algorithm follows an iterative process: initial random centroid selection, pixel assignment to nearest centroids using Euclidean distance calculation, centroid recalculation based on cluster members, and repetition until convergence. For image processing, pixels are typically represented in feature spaces such as RGB color space or CIE Lab color space for better segmentation results.

Key implementation considerations include preprocessing steps like image reshaping from 2D to feature vectors, post-processing for visualization, and methods for determining optimal k values using techniques like the elbow method or silhouette analysis. This approach effectively segments and extracts different objects or features within images, enabling more precise image classification and analysis for computer vision applications.