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Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comparing Images Using the Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Vectors from Algebraic Graph Theory
IEEE Transactions on Pattern Analysis and Machine Intelligence
Polyhedral object recognition by indexing
Pattern Recognition
From graphs to manifolds – weak and strong pointwise consistency of graph laplacians
COLT'05 Proceedings of the 18th annual conference on Learning Theory
Graph Characteristic from the Gauss-Bonnet Theorem
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Characterizing Graphs Using Spherical Triangles
IbPRIA '09 Proceedings of the 4th Iberian Conference on Pattern Recognition and Image Analysis
Graph Regularisation Using Gaussian Curvature
GbRPR '09 Proceedings of the 7th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition
Graph embedding using an edge-based wave Kernel
SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
Regularising the Ricci flow embedding
SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
SemaFor: semantic document indexing using semantic forests
Proceedings of the 21st ACM international conference on Information and knowledge management
Similarity in languages and programs
Theoretical Computer Science
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In this paper we study the manifold embedding of graphs resulting from the Young-Householder decomposition of the heat kernel [19]. We aim to explore how the sectional curvature associated with the embedding can be used as feature for the purposes of gauging the similarity of graphs, and hence clustering them. To gauging the similarity of pairs of graphs, we require a means of comparing sets of such features without explicit correspondences between the nodes of the graphs being considered. To this end, the Hausdorff distance, and a robust modified variant of the Hausdorff distance are used. we experiment on sets of graphs representing the proximity image features in different views of different objects. By applying multidimensional scaling to the Hausdorff distances between the different object views, we demonstrate that our sectional curvature representation is capable of clustering the different views of the same object together.