Local feature based image similarity functions for kNN classification (Contributo in atti di convegno)

Type
Label
  • Local feature based image similarity functions for kNN classification (Contributo in atti di convegno) (literal)
Anno
  • 2011-01-01T00:00:00+01:00 (literal)
Alternative label
  • Amato G., Falchi F. (2011)
    Local feature based image similarity functions for kNN classification
    in 3rd International Conference on Agents and Artificial Intelligence, ICAART 2011, Roma, Italy, 28-30 Gennaio 2011
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Amato G., Falchi F. (literal)
Pagina inizio
  • 157 (literal)
Pagina fine
  • 166 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#altreInformazioni
  • Progetto VIsual Support to Interactive TOurism in Tuscany. - Acronimo VISITO Tuscany. - Tipo Progetto NC. - Volume 1. - Area di valutazione 01 - Scienze matematiche e informatiche (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#titoloVolume
  • ICAART 2011 - Proceedings of the 3rd International Conference on Agents and Artificial Intelligence, Volume 1 (literal)
Note
  • PuMa (literal)
  • Scopu (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • CNR-ISTI, Pisa, Italy (literal)
Titolo
  • Local feature based image similarity functions for kNN classification (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn
  • 978-989-8425-40-9 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#curatoriVolume
  • Joaquim Filipe, Ana L. N. Fred (literal)
Abstract
  • Applications of image content recognition, as for instance landmark recognition, can be obtained by using techniques of kN N 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 kN N 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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