A Bayesian compatibility model for graph matching
Pattern Recognition Letters
An ontology-based comparative anatomy information system
Artificial Intelligence in Medicine
Models and algorithms for computing the common labelling of a set of attributed graphs
Computer Vision and Image Understanding
Graph attribute embedding via Riemannian submersion learning
Computer Vision and Image Understanding
Decision trees for error-tolerant graph database filtering
GbRPR'05 Proceedings of the 5th IAPR international conference on Graph-Based Representations in Pattern Recognition
Towards the unification of structural and statistical pattern recognition
Pattern Recognition Letters
Pattern analysis with graphs: Parallel work at Bern and York
Pattern Recognition Letters
Graph matching based on spectral embedding with missing value
Pattern Recognition
Geometric graph comparison from an alignment viewpoint
Pattern Recognition
Multi-label lego -- enhancing multi-label classifiers with local patterns
IDA'12 Proceedings of the 11th international conference on Advances in Intelligent Data Analysis
QoS-based approach for context-aware service selection with fuzzy preferences handling
International Journal of Computer Applications in Technology
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Relational models are frequently used in high-level computer vision. Finding a correspondence between a relational model and an image description is an important operation in the analysis of scenes. In this paper the process of finding the correspondence is formalized by defining a general relational distance measure that computes a numeric distance between any two relational descriptions-a model and an image description, two models, or two image descriptions. The distance measure is proved to be a metric, and is illustrated with examples of distance between object models. A variant measure used in our past studies is shown not to be a metric.