An energy function and continuous edit process for graph matching
Neural Computation
| Hi-index | 0.00 |
The aim of this paper is to provide a comparative evaluation of a number of contrasting approaches to relational matching. Unique to this study is the way in which we show how a diverse family of algorithms relate to one-another using a common Bayesian framework. Broadly speaking there are two main aspects to this study. Firstly we focus on the issue of how relational inexactness may be quantified. We illustrate that several popular relational distance measures can be recovered as specific limiting cases of the same Bayesian consistency measure. The second aspect of our comparison concerns the way in which structural inexactness is controlled. We investigate three different realisations of the matching process which draw on contrasting control models. The main conclusion of our study is that the active process of graph-editing outperforms the alternatives in terms of its ability to effectively control a large population of contaminating clutter.