Image Segmentation Using Combined Spectral Clustering and SVM

This document presents an image segmentation program combining spectral clustering and Support Vector Machines (SVM). Developed in MATLAB, the implementation utilizes spectral clustering algorithms which prove highly effective for data classification tasks. The spectral clustering approach leverages graph theory and matrix operations, treating data points as graph nodes while computing similarity metrics to partition them into distinct clusters. Key implementation steps include constructing similarity matrices using Gaussian kernel functions, performing eigenvalue decomposition on Laplacian matrices, and applying k-means clustering to the reduced spectral embeddings. Complementing this, the SVM component serves as a powerful classifier that employs kernel tricks for high-dimensional feature mapping, enabling superior class separation. The integration methodology involves using spectral clustering for initial segmentation followed by SVM refinement for boundary optimization. This hybrid approach yields significantly improved accuracy and reliability in image segmentation, particularly advantageous for large-scale data classification and partitioning scenarios. The MATLAB code structure incorporates functions like svmtrain for classification model development and custom implementations for graph Laplacian computation and eigenvalue analysis.