K-means Clustering Algorithm for Color Image Segmentation

This code implements K-means clustering algorithm for color image segmentation in MATLAB. The algorithm works by grouping image pixels into K distinct clusters based on their color features, effectively partitioning the image into meaningful regions. Key implementation aspects include: - Converting the input image to appropriate color space (typically RGB or LAB) - Reshaping the 3D image matrix into a 2D feature array where each row represents a pixel's color values - Applying MATLAB's built-in kmeans function to cluster pixels based on Euclidean distance minimization - Using cluster centroids to reassign pixel values and generate segmented output The implementation leverages MATLAB's image processing toolbox for efficient matrix operations and visualization. Techniques such as color space conversion and feature normalization ensure segmentation accuracy. This approach helps identify distinct image regions and extract valuable information, providing a foundation for subsequent image analysis tasks like object recognition and computer vision applications.