K-Means Image Segmentation Algorithm Implementation in MATLAB

This implementation demonstrates k-means image segmentation algorithm using MATLAB. The k-means algorithm requires the input parameter k, which specifies the number of clusters to partition n data objects into. The clustering process ensures that objects within the same cluster have high similarity, while objects from different clusters maintain low similarity. The algorithm computes cluster similarity by calculating centroids (gravitational centers) derived from the mean values of objects in each cluster. In practical applications, the k-means algorithm is widely used in image segmentation and data mining. By clustering pixels in an image into distinct groups, effective image segmentation can be achieved. The segmented images can then be utilized for object detection, image recognition, and related computer vision tasks. Additionally, k-means serves as a powerful tool for cluster analysis in data mining, helping uncover hidden patterns and structures within datasets. A critical step in the k-means algorithm involves computing cluster similarity through centroid calculation. By determining the mean values of objects within each cluster, we obtain representative "centroid objects" that act as gravitational centers attracting other objects in the same cluster. The low similarity between objects from different clusters indicates significant differentiation between cluster groups. In MATLAB implementation, key functions include kmeans() for clustering operations and imsegkmeans() specifically designed for image segmentation tasks. The algorithm typically follows these steps: initialize k centroids randomly, assign each pixel to the nearest centroid based on distance metrics (usually Euclidean distance), recalculate centroids from assigned pixels, and iterate until centroid positions stabilize. In summary, k-means is a fundamental clustering algorithm that plays vital roles in image segmentation and data mining applications. By grouping data objects into clusters with high internal similarity, it facilitates better understanding and analysis of complex datasets. In practical implementations, appropriate k values should be selected based on specific requirements and data characteristics to achieve optimal clustering results.