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
  • 25 (literal)
Pagina fine
  • 32 (literal)
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
data.CNR.it