Image segmentation using spectral clustering of Gaussian mixture models

• We combine the EM algorithm for GMM and spectral clustering for image segmentation.
• Our EM-IRC method produces much fewer Gaussian components than the traditional methods.
• We use a revised Floyd׳s algorithm to produce block-structured similarity matrices.
• Spectral clustering merges Gaussian components based on the similarity matrices.

A novel image segmentation method that combines spectral clustering and Gaussian mixture models is presented in this paper. The new method contains three phases. First, the image is partitioned into small regions modeled by a Gaussian Mixture Model (GMM), and the GMM is solved by an Expectation–Maximization (EM) algorithm with a newly proposed Image Reconstruction Criterion, named EM-IRC. Second, the distances among the GMM components are measured using Kullback–Leibler (KL) divergence, and a revised Floyd׳s algorithm developed from Zadeh׳s operations is used to build the similarity matrix based on those distances. Finally, spectral clustering is applied to this improved similarity matrix to merge the GMM components, i.e., the corresponding small image regions, to obtain the final segmentation result. Our contributions include the new EM-IRC algorithm, the revised Floyd׳s algorithm, and the novel overall framework. The experimental evaluation on the IRIS dataset and the real-world image segmentation problem demonstrates the effectiveness of our proposed approach.