Color Clustering Using K-means Algorithm

This implementation utilizes the K-means clustering algorithm to group image pixels based on color similarity. The algorithm works by first converting the image into a feature space where each pixel is represented by its color values (typically in RGB or LAB color space). The K-means algorithm then iteratively assigns pixels to the nearest cluster centroids and updates these centroids until convergence. Key implementation aspects include: - Preprocessing the image into a 2D array where each row represents a pixel's color features - Using Euclidean distance to measure color similarity between pixels and cluster centers - Implementing the standard K-means workflow: initialization, assignment, and update steps - Allowing customizable cluster numbers (K value) for different granularity requirements The clustering results enable various applications including image editing, content-based image retrieval, and visual analysis. To perform color clustering on different images, users simply need to replace the test image file while maintaining the same processing pipeline. This implementation demonstrates flexibility and scalability, effectively handling diverse image types while producing accurate clustering outcomes through proper parameter tuning and color space selection.