Geometric consistency checks for kNN based image classification relying on local features (Contributo in atti di convegno)

Type
Label
  • Geometric consistency checks for kNN based image classification relying on local features (Contributo in atti di convegno) (literal)
Anno
  • 2011-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1145/1995412.1995428 (literal)
Alternative label
  • Amato G., Falchi F., Gennaro C. (2011)
    Geometric consistency checks for kNN based image classification relying on local features
    in Fourth International Conference on SImilarity Search and APplications, SISAP 2011, Lipari, Italia, 30 Giugno - 1 Luglio 2011
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Amato G., Falchi F., Gennaro C. (literal)
Pagina inizio
  • 81 (literal)
Pagina fine
  • 88 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#altreInformazioni
  • Progetto VIsual Support to Interactive TOurism in Tuscany. - Acronimo VISITO Tuscany. - Area di valutazione 01 - Scienze matematiche e informatiche (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://dl.acm.org/citation.cfm?id=1995428 (literal)
Note
  • Scopu (literal)
  • PuMa (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • CNR-ISTI, Pisa, Italy (literal)
Titolo
  • Geometric consistency checks for kNN based image classification relying on local features (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn
  • 978-1-4503-0795-6 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#curatoriVolume
  • Alfredo Ferro (literal)
Abstract
  • Applications of image content recognition, as for instance landmark recognition, can be obtained by using techniques of kNN classifications based on the use of local image features, such as SIFT or SURF. Quality of image classification can be improved by defining geometric consistency check rules based on space transformations of the scene depicted in images. However, this prevents the use of state of the art access methods for similarity searching and sequential scan of the images in the training sets has to be executed in order to perform classification. In this paper we propose a technique that allows one to use access methods for similarity searching, such as those exploiting metric space properties, in order to perform kNN classification with geometric consistency checks. We will see that the proposed approach, in addition to offer an obvious efficiency improvement, surprisingly offers also an improvement of the effectiveness of the classification (literal)
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