Pattern Vectors from Algebraic Graph Theory
IEEE Transactions on Pattern Analysis and Machine Intelligence
Defining implicit objective functions for design problems
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Graph matching using the interference of discrete-time quantum walks
Image and Vision Computing
Coined quantum walks lift the cospectrality of graphs and trees
Pattern Recognition
A Bag of Strings Representation for Image Categorization
Journal of Mathematical Imaging and Vision
A graph-spectral method for surface height recovery
Pattern Recognition
Towards unitary representations for graph matching
GbRPR'05 Proceedings of the 5th IAPR international conference on Graph-Based Representations in Pattern Recognition
Coined quantum walks lift the cospectrality of graphs and trees
EMMCVPR'05 Proceedings of the 5th international conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
Graph structure analysis based on complex network
Digital Signal Processing
Coloring based approach for matching unrooted and/or unordered trees
Pattern Recognition Letters
| Hi-index | 0.00 |
This paper is concerned with computing graph edit distance. One ofthe criticisms that can be leveled at existing methods forcomputing graph edit distance is that it lacks the formality andrigour of the computation of string edit distance. Hence, our aimis to convert graphs to string sequences so that standard stringedit distance techniques can be used. To do this we use graphspectral seriation method to convert the adjacency matrix into astring or sequence order. We pose the problem of graph-matching asmaximum aposteriori probability alignment of the seriationsequences for pairs of graphs. This treatment leads to anexpression for the edit costs. We compute the edit distance byfinding the sequence of string edit operations which minimise thecost of the path traversing the edit lattice. The edit costs aredefined in terms of the a posteriori probability of visiting a siteon the lattice. We demonstrate the method with results on adata-set of Delaunay graphs.