An Image Understanding System Using Attributed Symbolic Representation and Inexact Graph-Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Illumination Planning for Object Recognition Using Parametric Eigenspaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Organizing Large Structural Modelbases
IEEE Transactions on Pattern Analysis and Machine Intelligence
Correction to "Pairwise Data Clustering by Deterministic Annealing"
IEEE Transactions on Pattern Analysis and Machine Intelligence
Spectral Feature Vectors for Graph Clustering
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Computer Science Review
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In this paper we explore how to use spectral methods for embedding and clustering unweighted graphs. We use the leading eigenvectors of the graph adjacency matrix to define eigenmodes of the adjacency matrix. For each eigenmode, we compute vectors of spectral properties. These include the eigenmode perimeter, eigenmode volume, Cheeger number, inter-mode adjacency matrices and intermode edge-distance. We embed these vectors in a pattern-space using two contrasting approaches. The first of these involves performing principal or independent components analysis on the covariance matrix for the spectral pattern vectors. The second approach involves performing multidimensional scaling on the L2 norm for pairs of pattern vectors. We illustrate the utility of the embedding methods on neighbourhood graphs representing the arrangement of corner features in 2D images of 3D polyhedral objects.