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A Generalized Metric Distance between Hierarchically Partitioned Images

Jomier, Geneviève; Manouvrier, Maude; Rukoz, Marta (2005), A Generalized Metric Distance between Hierarchically Partitioned Images, in Masseglia, Florent, Proceedings of the 6th international workshop on Multimedia data mining: mining integrated media and complex data, ACM, p. 33-41

Type
Communication / Conférence
Date
2005
Conference country
UNITED STATES
Book title
Proceedings of the 6th international workshop on Multimedia data mining: mining integrated media and complex data
Book author
Masseglia, Florent
Publisher
ACM
ISBN
1-59593-216-X
Pages
33-41
Metadata
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Author(s)
Jomier, Geneviève
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Manouvrier, Maude cc
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Rukoz, Marta
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Abstract (EN)
This article presents a generalized metric distance, called $\Delta$-distance, between images represented by a tree structure resulting from a recursive image partition. This distance is used to perform content-based image retrieval queries in databases. Delta-distance allows to retrieve images globally similar to a query image. This distance takes into account the location of the image visual features. It can be performed using a multi-level filtering algorithm. Moreover, Delta-distance allows region-based queries. In this case, the resulting images contain quadrants similar to the quadrants selected by the user in the query image or contain quadrants similar to the entire query image. Because it is a generalized distance function, some particular cases of the $\Delta$-distance appear in existing content-based image retrieval systems.
Subjects / Keywords
recursive image decomposition; quad/quin/nona-tree; Image database; content-based image retrieval; multi-level filtering; image region similarity

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