Defining consistency to detect change using inexact graph matching

Authors:
Sidharta Gautama;Werner Goeman;Johan D'Haeyer
Affiliations:
TELIN, Ghent University, Gent, Belgium;TELIN, Ghent University, Gent, Belgium;TELIN, Ghent University, Gent, Belgium
Venue:
GbRPR'05 Proceedings of the 5th IAPR international conference on Graph-Based Representations in Pattern Recognition
Year:
2005

Citing 4
Cited 0

Markov random field modeling in computer vision

Markov random field modeling in computer vision
Feasible and infeasible maxima in a quadratic program for maximum clique

Journal of Artificial Neural Networks - Special issue: neural networks for optimization
Structural Matching in Computer Vision Using Probabilistic Relaxation

IEEE Transactions on Pattern Analysis and Machine Intelligence
On the Foundations of Relaxation Labeling Processes

IEEE Transactions on Pattern Analysis and Machine Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we discuss the notion of consistency in inexact graph matching to be able to correctly determine the optimal solution of the matching problem. Consistency allows us to study the cost function which controls the graph matching process, regardless of the optimization technique that is used. The analysis is performed in the context of change detection in geospatial information. A condition based on the expected graph error is presented which allows to determine the bounds of error tolerance and in this way characterizes acceptable over inacceptable data inconsistencies.