Leibe, Bastian ; Mikolajczyk, Krystian ; Schiele, Bernt (2006)
Efficient clustering and matching for object glass.
17th British Machine Vision Conference (BMVC06). Edinburgh (04.09.2007-07.09.2007)
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
In this paper we address the problem of building object class representations based on local features and fast matching in a large database. We propose an efficient algorithm for hierarchical agglomerative clustering. We examine different agglomerative and partitional clustering strategies and compare the quality of obtained clusters. Our combination of partitional-agglomerative clustering gives significant improvement in terms of efficiency while maintaining the same quality of clusters. We also propose a method for building data structures for fast matching in high dimensional feature spaces. These improvements allow to deal with large sets of training data typically used in recognition of multiple object classes.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2006 |
| Autor(en): | Leibe, Bastian ; Mikolajczyk, Krystian ; Schiele, Bernt |
| Art des Eintrags: | Bibliographie |
| Titel: | Efficient clustering and matching for object glass |
| Sprache: | Englisch |
| Publikationsjahr: | 2006 |
| Ort: | Edinburgh |
| Verlag: | BMVA |
| Buchtitel: | British Machine Vision Conference 2006, BMVC06. Proceedings |
| Veranstaltungstitel: | 17th British Machine Vision Conference (BMVC06) |
| Veranstaltungsort: | Edinburgh |
| Veranstaltungsdatum: | 04.09.2007-07.09.2007 |
| Kurzbeschreibung (Abstract): | In this paper we address the problem of building object class representations based on local features and fast matching in a large database. We propose an efficient algorithm for hierarchical agglomerative clustering. We examine different agglomerative and partitional clustering strategies and compare the quality of obtained clusters. Our combination of partitional-agglomerative clustering gives significant improvement in terms of efficiency while maintaining the same quality of clusters. We also propose a method for building data structures for fast matching in high dimensional feature spaces. These improvements allow to deal with large sets of training data typically used in recognition of multiple object classes. |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Multimodale Interaktive Systeme |
| Hinterlegungsdatum: | 20 Nov 2008 08:25 |
| Letzte Änderung: | 28 Nov 2024 11:34 |
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