Image Segmentation using K-means Clustering Algorithm

Image segmentation is a fundamental image processing technique that partitions an image into distinct regions to extract target objects. One widely adopted approach is the K-means clustering algorithm, which groups image pixels into different clusters based on their similarity features. The algorithm implementation typically involves several key steps: initializing K cluster centers, computing pixel-to-centroid distances using Euclidean or color space metrics, reassigning pixels to nearest clusters, and iteratively updating centroids until convergence. This method enables precise image segmentation through optimized cluster assignments and can be efficiently implemented using libraries like OpenCV or scikit-learn with functions such as cv2.kmeans() or sklearn.cluster.KMeans(). K-means clustering for image segmentation finds extensive applications across various domains including computer vision systems, medical image analysis, remote sensing, and industrial inspection.