Non-bayesian Graph Matching without Explicit Compatibility Calculations

Authors:
Barend J. van Wyk;Michaël A. van Wyk
Affiliations:
-;-
Venue:
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Year:
2002

Citing 11
Cited 1

An Eigendecomposition Approach to Weighted Graph Matching Problems

IEEE Transactions on Pattern Analysis and Machine Intelligence
A Bayesian compatibility model for graph matching

Pattern Recognition Letters
A Graduated Assignment Algorithm for Graph Matching

IEEE Transactions on Pattern Analysis and Machine Intelligence
Structural Matching by Discrete Relaxation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple graph matching with Bayesian inference

Pattern Recognition Letters - special issue on pattern recognition in practice V
Graph Matching With a Dual-Step EM Algorithm

IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimization by Vector Space Methods

Optimization by Vector Space Methods
A Linear Programming Approach for the Weighted Graph Matching Problem

IEEE Transactions on Pattern Analysis and Machine Intelligence
Structural Matching in Computer Vision Using Probabilistic Relaxation

IEEE Transactions on Pattern Analysis and Machine Intelligence
A RKHS Interpolator-Based Graph Matching Algorithm

IEEE Transactions on Pattern Analysis and Machine Intelligence
A framework for multiscale and hybrid RKHS-based approximators

IEEE Transactions on Signal Processing

Orthonormal Kernel Kronecker product graph matching

GbRPR'03 Proceedings of the 4th IAPR international conference on Graph based representations in pattern recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper introduces a novel algorithm for performing Attributed Graph Matching (AGM). A salient characteristic of the Interpolator- Based Kronecker Product Graph Matching (IBKPGM) algorithm is that it does not require the explicit calculation of compatibility values between vertices and edges, either using compatibility functions or probability distributions. No assumption is made about the adjacency structure of the graphs to be matched. The IBKPGM algorithm uses Reproducing Kernel Hilbert Space (RKHS) interpolator theory to obtain an unconstrained estimate to the Kronecker Match Matrix (KMM) from which a permutation sub-matrix is inferred.