Kernel Function-Based C-Means Clustering for Image Segmentation

The kernel function-based C-means clustering image segmentation algorithm can be effectively implemented using MATLAB. This algorithm employs kernel methods to map data to a higher-dimensional feature space, enabling better separation of non-linearly separable image regions. The implementation typically involves: Key MATLAB functions and implementation steps: - Preprocessing using imread() to load image data and im2double() for normalization - Applying kernel transformations (such as Gaussian RBF or polynomial kernels) to enhance feature separation - Utilizing kmeans() function with custom distance metrics adapted for kernel space - Post-processing with region labeling and boundary detection using bwlabel() and edge detection functions The algorithm demonstrates excellent performance in segmenting complex images by effectively identifying different objects or regions through improved cluster separation in kernel-induced feature spaces. MATLAB's comprehensive image processing toolbox provides essential functions for image analysis, including statistical feature extraction and visualization tools like imagesc() and contourf() for result evaluation. This makes MATLAB an ideal platform for implementing and testing kernel-based clustering approaches for image segmentation tasks.