MATLAB Implementation of Color Image Segmentation Using K-means Clustering

Color image segmentation proves exceptionally effective in practice. This function implements k-means clustering on input RGB images (dimensions m x n x 3). Users must provide at least two parameters: IMGIN (input image matrix) and NCLUSTERS (number of color clusters). The function then performs interactive color segmentation using k-means clustering algorithm. The interactivity comes from prompting users to click on target colors within the image to define segmentation regions - this generates initial cluster centers for the k-means optimization process.

Beyond core functionality, the code includes several advanced features. It automatically detects image boundaries using edge detection algorithms (like Sobel or Canny) and incorporates them into the segmentation mask. Users can fine-tune segmentation results by adjusting k-means parameters such as maximum iterations, distance metric (Euclidean/squared Euclidean), and replication count for initial centroid positions. The implementation also supports multiple color space transformations through MATLAB's image processing toolbox functions, converting RGB images to HSV or CIELab color spaces where perceptual color differences may yield better segmentation accuracy.

Overall, this function provides a straightforward yet powerful approach for color image segmentation, enabling users to obtain precise segmentation results efficiently through both automated clustering and interactive color selection mechanisms.