MATLAB Implementation of Image Segmentation using K-means Clustering

In this practical example, we will implement image segmentation using the K-means clustering algorithm. We will leverage MATLAB's built-in library functions to accomplish this task, with detailed code annotations to ensure clear comprehension. While this approach maintains simplicity and accessibility, it demonstrates significant power in distinguishing different regions within images, thereby enhancing the precision and efficiency of your image processing workflows. The implementation primarily utilizes MATLAB's kmeans() function, which clusters image pixels based on their color or intensity values into a specified number of groups. We'll convert the input image to appropriate color space (typically LAB space for better color separation), reshape the pixel data for clustering, and then reconstruct the segmented image. Key steps include data preprocessing, cluster centroid initialization, iterative distance calculations using Euclidean metric, and pixel reassignment. Let's begin exploring this practical image segmentation technique!