http://www.cnr.it/ontology/cnr/individuo/prodotto/ID215133
Learning Kernels on Extended Reeb Graphs for 3D shape classification and retrieval (Contributo in atti di convegno)
- Type
- Label
- Learning Kernels on Extended Reeb Graphs for 3D shape classification and retrieval (Contributo in atti di convegno) (literal)
- Anno
- 2013-01-01T00:00:00+01:00 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
- 10.2312/3DOR/3DOR13/025-032 (literal)
- Alternative label
V. Barra and S. Biasotti (2013)
Learning Kernels on Extended Reeb Graphs for 3D shape classification and retrieval
in Eurographics Workshop on 3D Object Retrieval, Girona, Spain, 11 Maggio 2013
(literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
- V. Barra and S. Biasotti (literal)
- Pagina inizio
- Pagina fine
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#titoloVolume
- Eurographics Workshop on 3D Object Retrieval (2013) (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
- Clermont-Université, Université Blaise Pascal, LIMOS, BP 10448, F-63000 CLERMONT-FERRAND
CNRS, UMR 6158, LIMOS, F-63173 AUBIERE
Istituto di Matematica Applicata e Tecnologie Informatiche 'E. Magenes', CNR, Italy (literal)
- Titolo
- Learning Kernels on Extended Reeb Graphs for 3D shape classification and retrieval (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn
- 978-3-905674-44-6 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#curatoriVolume
- S. Biasotti, I. Pratikakis, U. Castellani, T. Schreck, A. Godil, and R. Veltkamp (literal)
- Abstract
- This paper addresses 3D shape classification and retrieval in terms of supervised selection of the most significant
features in a space of attributed graphs encoding different shape characteristics. For this purpose, 3D models
are represented as bags of shortest paths defined over well chosen Extended Reeb graphs, while the similarity
between pairs of Extended Reeb graphs is addressed through kernels adapted to these descriptions. Given this
set of kernels, a Multiple Kernel Learning algorithm is used to find an optimal linear combination of kernels
for classification and retrieval purposes. Results are comparable with the best results of the literature, and the
modularity and flexibility of the kernel learning ensure its applicability to a large set of methods (literal)
- Editore
- Prodotto di
- Autore CNR
- Insieme di parole chiave
Incoming links:
- Prodotto
- Autore CNR di
- Editore di
- Insieme di parole chiave di