Object recognition by clustering spectral features

Bin Luo, Richard C. Wilson, Edwin R. Hancock

Abstract: We investigate whether vectors of graph spectral features can be used for the purposes of graph clustering. We commence from the eigenvalues and eigenvectors of the adjacency matrix. Each of the leading eigenmodes represents a cluster of nodes and is mapped to a component of a feature vector. The spectral features used as components of the vectors are the eigenvalues and the shared perimeter length. We explore whether these vectors can be used for the purposes of graph clustering. Here we investigate the use of both central and pairwise clustering methods. On a database of view-graphs, both of the features provide good clusters while the eigenvectors perform better.