An Eigendecomposition Approach to Weighted Graph Matching Problems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature-based correspondence: an eigenvector approach
Image and Vision Computing - Special issue: BMVC 1991
Active shape models—their training and application
Computer Vision and Image Understanding
SIAM Review
Exploiting sparsity in primal-dual interior-point methods for semidefinite programming
Mathematical Programming: Series A and B - Special issue: papers from ismp97, the 16th international symposium on mathematical programming, Lausanne EPFL
Shock Graphs and Shape Matching
International Journal of Computer Vision
Structural Graph Matching Using the EM Algorithm and Singular Value Decomposition
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Correspondence Matching with Modal Clusters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Analysis of Planar Shapes Using Geodesic Paths on Shape Spaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Vectors from Algebraic Graph Theory
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discovering Shape Classes using Tree Edit-Distance and Pairwise Clustering
International Journal of Computer Vision
A spectral generative model for graph structure
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical 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
Shape classification by manifold learning in multiple observation spaces
Information Sciences: an International Journal
| Hi-index | 0.01 |
Shape analysis played important role in computer vision based tasks. The importance of shape information relies that it usually contains perceptual information, and thus can be used for high level visual information analysis. Currently, there are many ways that shapes can be represented as a structural manner using graphs. Hence shapes can be analyzed by using graph methods. This paper describes how graph-spectral methods can be used to transform the node correspondence problem into one of point-sets alignment. We commence by using the ISOMAP algorithm to embed the nodes of a graph in a low-dimensional Euclidean space. With the nodes in the graph transformed to points in a metric space, we can recast the problem of graph-matching into that of aligning the point-sets. Here we use semidefinite programming to develop a robust point-sets correspondences algorithm. Variations in graph structure using the covariance matrix for corresponding embedded point-positions is captured. We construct a statistical point distribution model for the embedded node positions using the eigenvalues and eigenvectors of the covariance matrix. We show how to use this model to project individual graph, i.e. shape into the eigenspace of the point position covariance matrix. We illustrate the utility of the resulting method for shape analysis and recognition on COIL and MPEG-7 databases.