A linear generative model for graph structure

Authors:
Bin Luo;Richard C. Wilson;Edwin R. Hancock
Affiliations:
School of Computer Science and Technology, Anhui University, P.R.China;Department of Computer Science, University of York, York, UK;Department of Computer Science, University of York, York, UK
Venue:
GbRPR'05 Proceedings of the 5th IAPR international conference on Graph-Based Representations in Pattern Recognition
Year:
2005

Citing 7
Cited 0

Illumination Planning for Object Recognition Using Parametric Eigenspaces

IEEE Transactions on Pattern Analysis and Machine Intelligence
Active shape models—their training and application

Computer Vision and Image Understanding
Learning Bayesian Networks: The Combination of Knowledge and Statistical Data

Machine Learning
Structural Matching by Discrete Relaxation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Structural Graph Matching Using the EM Algorithm and Singular Value Decomposition

IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
BioID: A Multimodal Biometric Identification System

Computer
Structural Matching in Computer Vision Using Probabilistic Relaxation

IEEE Transactions on Pattern Analysis and Machine Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper shows how to construct a linear deformable model for graph structure by performing principal components analysis (PCA) on the vectorised adjacency matrix. We commence by using correspondence information to place the nodes of each of a set of graphs in a standard reference order. Using the correspondences order, we convert the adjacency matrices to long-vectors and compute the long-vector covariance matrix. By projecting the vectorised adjacency matrices onto the leading eigenvectors of the covariance matrix, we embed the graphs in a pattern-space. We illustrate the utility of the resulting method for shape-analysis.