Evaluation of Spectral-Based Methods for Median Graph Computation
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part II
Exact Median Graph Computation Via Graph Embedding
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Median graph: A new exact algorithm using a distance based on the maximum common subgraph
Pattern Recognition Letters
Median graphs: A genetic approach based on new theoretical properties
Pattern Recognition
IbPRIA '09 Proceedings of the 4th Iberian Conference on Pattern Recognition and Image Analysis
A Recursive Embedding Approach to Median Graph Computation
GbRPR '09 Proceedings of the 7th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition
Finding semantic structures in image hierarchies using Laplacian graph energy
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
A generic framework for median graph computation based on a recursive embedding approach
Computer Vision and Image Understanding
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Generative models are well known in the domain of statistical pattern recognition. Typically, they describe the probability distribution of patterns in a vector space. The individual patterns are defined by vectors and so the individual features of the pattern are well defined. In contrast, very little has been done with generative models of graphs. Graphs are not naturally represented in a vector space since there is no natural labelling of the vertices of the graphs - different labellings lead to different representations of the graph structure. Because of this, simple statistical quantities such as mean and variance are difficult to define for a group of graphs. While we can define statistical quantities of individual edges, it is not so straightforward to define how sets of edges in graphs are related. The spectral decomposition of a graph can be used to extract information about the relationship of edges and parts in a graph. In this paper we look at the problem of mixing graphs by using the spectral representation of a graph as an intermediate step. The spectral representation allows us to mix different structural features from each of the graphs to create new combinations. We can also define an averaging process on the spectral representations which generates a graph close to the graph median.