High-dimensional Spectral Feature Selection for 3D Object Recognition based on Reeb Graphs (Contributo in atti di convegno)

Type
Label
  • High-dimensional Spectral Feature Selection for 3D Object Recognition based on Reeb Graphs (Contributo in atti di convegno) (literal)
Anno
  • 2010-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1007/978-3-642-14980-1_11 (literal)
Alternative label
  • Bonev B.; Escolano F.; Giorgi D.; Biasotti S. (2010)
    High-dimensional Spectral Feature Selection for 3D Object Recognition based on Reeb Graphs
    in +SSPR 2010: Joint IAPR International Workshops on Structural and Syntactic Pattern Recognition (SSPR 2010) and Statistical Techniques in Pattern Recognition (SPR 2010), Cesme, Izmir, Turkey, 18-20 Agosto 2010
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Bonev B.; Escolano F.; Giorgi D.; Biasotti S. (literal)
Pagina inizio
  • 119 (literal)
Pagina fine
  • 128 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#titoloVolume
  • Structural, Syntactic, and Statistical Pattern Recognition (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#volumeInCollana
  • 6218 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#note
  • Published in: Lecture Notes in Computer Science, 2010, Volume 6218, pp. 119-128, doi: 10.1007/978-3-642-14980-1_11 (literal)
Note
  • Scopu (literal)
  • SpringerLink (literal)
  • ISI Web of Science (WOS) (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • Bonev, Escolano: University of Alicante, Spain Giorgi, Biasotti: IMATI CNR, Genova, Italy (literal)
Titolo
  • High-dimensional Spectral Feature Selection for 3D Object Recognition based on Reeb Graphs (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn
  • 978-3-642-14979-5 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#curatoriVolume
  • Hancock E. R.; Wilson R. C.; Windeatt T.; Ulusoy I.; Escolano F. (literal)
Abstract
  • In this work we evaluate purely structural graph measures for 3D object classification. We extract spectral features from different Reeb graph representations and successfully deal with a multi-class problem. We use an information-theoretic filter for feature selection. We show experimentally that a small change in the order of selection has a significant impact on the classification performance and we study the impact of the precision of the selection criterion. A detailed analysis of the feature participation during the selection process helps us to draw conclusions about which spectral features are most important for the classification problem. (literal)
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