Spectral Clustering Algorithm for Image Segmentation with MATLAB Implementation

In this document, we explore the application of spectral clustering algorithm for image segmentation, with a focus on its MATLAB implementation. Spectral clustering is a widely-used clustering technique that partitions data into distinct groups based on similarity measures. The algorithm is grounded in graph theory and linear algebra principles, making it particularly effective in image processing applications. Through spectral clustering, we can efficiently segment images into coherent regions, enabling better interpretation and manipulation of visual data.

The technical implementation involves several key steps: First, we construct a similarity graph from image pixels using Gaussian kernel functions to measure pairwise affinities. The MATLAB code typically utilizes functions like pdist2 for distance computation and exp for kernel transformation. Next, we compute the graph Laplacian matrix and perform eigenvalue decomposition using MATLAB's eigs function. The algorithm then employs k-means clustering (via kmeans function) on the eigenvectors to obtain final segmentation labels. We will provide comprehensive MATLAB code examples demonstrating how to handle image preprocessing, similarity matrix construction, spectral embedding, and clustering validation, including practical considerations for parameter tuning and computational efficiency.