This project applies unsupervised learning techniques to segment a football match image into distinct visual components. There is a comparison of K-Means and CLARA clustering algorithms, used to identify optimal color groups, validated through Silhouette analysis. The resulting high-dimensional data is visualized using PCA and t-SNE projections to provide a comprehensive look at the image's color structure.