## Inexact graph matching using learning and simulation of Bayesian networks. An empirical comparison between different approaches with synthetic data (2000)

Citations: | 6 - 1 self |

### BibTeX

@MISC{Bengoetxea00inexactgraph,

author = {E. Bengoetxea and P. Larrañaga and I. Bloch and A. Perchant and C. Boeres},

title = {Inexact graph matching using learning and simulation of Bayesian networks. An empirical comparison between different approaches with synthetic data},

year = {2000}

}

### OpenURL

### Abstract

Estimation Distribution Algorithms (EDAs) is a quite recent topic in optimisation techniques. Several algorithms and approaches have already been proposed by different authors, but up to now there are very few papers showing their potential and comparing them to other evolutionary computation methods and algorithms such as Genetic Algorithms (GAs). A problem such as inexact graph matching is NP-hard and requires techniques that approximate to an acceptable solution. This problem arises when a non bijective correspondence is searched between two graphs G1 and G2 . A typical instance of this problem corresponds to the case where G1 is a model of the scene, and G2 is a graph derived from data (e.g. an image of the scene). EDA algorithms are well suited for this type of problems. This paper proposes to use EDA algorithms as a new approach for inexact graph matching. Also, two adaptations of the EDA approach to problems with constraints are described on the form of two techniques to cont...