Classification with Nonmetric Distances: Image Retrieval and Class Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
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MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
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Journal of Visual Languages and Computing
Graph Classification and Clustering Based on Vector Space Embedding
Graph Classification and Clustering Based on Vector Space Embedding
Entropy of Feature Point-Based Retina Templates
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Fingerprints as Spatial Graphs: Nodes and Edges
DICTA '11 Proceedings of the 2011 International Conference on Digital Image Computing: Techniques and Applications
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We construct a new family of normalised metrics for measuring the dissimilarity of finite sets in terms of the sizes of the sets and of their intersection. The family normalises a set-based analogue of the Minkowski metric family. It is parametrised by a real variable p=1, is monotonic decreasing in p, equals the normalised set difference metric when p=1 and equals the normalised maximum difference metric in the limit p-~. These metrics are suitable for comparison of finite sets in any context. Several applications to comparison of finite graphs are described.