String Edit Distance, Random Walks and Graph Matching
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Indexing through laplacian spectra
Computer Vision and Image Understanding
Graph matching using the interference of continuous-time quantum walks
Pattern Recognition
Graph matching using the interference of discrete-time quantum walks
Image and Vision Computing
An ontology-based comparative anatomy information system
Artificial Intelligence in Medicine
Pattern analysis with graphs: Parallel work at Bern and York
Pattern Recognition Letters
Geometric graph comparison from an alignment viewpoint
Pattern Recognition
Hypergraph-based image retrieval for graph-based representation
Pattern Recognition
The nearest neighbor problem in an abstract metric space
Pattern Recognition Letters
Hi-index | 0.14 |
Relational models are commonly used in scene analysis systems. Most such systems are experimental and deal with only a small number of models. Unknown objects to be analyzed are usually sequentially compared to each model. In this paper, we present some ideas for organizing a large database of relational models. We define a simple relational distance measure, prove it is a metric, and using this measure, describe two organizational/access methods: clustering and binary search trees. We illustrate these methods with a set of randomly generated graphs.