Implementation of Data Clustering Segmentation for Image Processing

This program initiates the image processing workflow by converting images from RGB color space to HSI color space. The conversion to HSI space facilitates more effective image segmentation and boundary synthesis processing due to its separation of luminance and chrominance components. For image segmentation, we implement color image segmentation strategies combined with the Mean Shift algorithm, which operates through kernel density estimation to identify clusters by iteratively shifting points toward higher density regions. These methods enable accurate partitioning of images into distinct regions based on color and texture characteristics. The boundary synthesis phase processes the segmented image by merging boundaries between different regions, employing algorithms that ensure smooth transitions and natural continuity throughout the image. The primary implementation resides in the 'keyprogram.m' file, which coordinates the entire segmentation pipeline, while the 'meanshift.m' file contains the core clustering function that handles data point convergence through bandwidth parameters and iteration controls. This integrated approach enables our program to process images more effectively, achieving precise segmentation results with naturally blended boundaries through systematic color space transformation, density-based clustering, and post-processing synthesis techniques.