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» Measuring the Similarity of Labeled Graphs
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CVPR
2011
IEEE
13 years 5 months ago
Learning invariance through imitation
Supervised methods for learning an embedding aim to map high-dimensional images to a space in which perceptually similar observations have high measurable similarity. Most approac...
Graham Taylor, Ian Spiro, Rob Fergus, Christoph Br...
ICMCS
2005
IEEE
153views Multimedia» more  ICMCS 2005»
14 years 3 months ago
Improved semantic region labeling based on scene context
Semantic region labeling in outdoor scenes, e.g., identifying sky, grass, foliage, water, and snow, facilitates content-based image retrieval, organization, and enhancement. A maj...
Matthew R. Boutell, Jiebo Luo, Christopher M. Brow...
GBRPR
2007
Springer
14 years 3 months ago
Bipartite Graph Matching for Computing the Edit Distance of Graphs
In the field of structural pattern recognition graphs constitute a very common and powerful way of representing patterns. In contrast to string representations, graphs allow us to ...
Kaspar Riesen, Michel Neuhaus, Horst Bunke
PVLDB
2010
110views more  PVLDB 2010»
13 years 8 months ago
Graph Homomorphism Revisited for Graph Matching
In a variety of emerging applications one needs to decide whether a graph G matches another Gp, i.e., whether G has a topological structure similar to that of Gp. The traditional ...
Wenfei Fan, Jianzhong Li, Shuai Ma, Hongzhi Wang, ...
CP
2009
Springer
14 years 10 months ago
Constraint-Based Graph Matching
Measuring graph similarity is a key issue in many applications. We propose a new constraint-based modeling language for defining graph similarity measures by means of constraints. ...
Christine Solnon, Vianney le Clément, Yves ...