Spectral Clustering Algorithm Implementation in MATLAB for Image Segmentation

The spectral clustering algorithm for image segmentation is implemented using MATLAB as the computational platform. Spectral clustering represents a widely-used image segmentation methodology that groups similar pixels into common clusters through advanced clustering techniques. The MATLAB implementation leverages image feature vectors for computational processing and cluster assignment, utilizing key functions such as eig() for eigenvalue decomposition and kmeans() for final clustering. This algorithm operates by constructing a similarity graph from image data, computing the graph Laplacian matrix, performing spectral decomposition to obtain eigenvectors, and finally applying k-means clustering in the reduced-dimensional spectral space. Through spectral clustering, image segmentation tasks can be achieved more effectively, significantly enhancing both the processing efficiency and segmentation accuracy of digital images. The implementation typically involves steps including similarity matrix construction, Laplacian matrix calculation, eigenvalue decomposition, and cluster assignment using MATLAB's built-in linear algebra and clustering functions.