On kNN classification and local feature based similarity functions (Contributo in volume (capitolo o saggio))

Type
Label
  • On kNN classification and local feature based similarity functions (Contributo in volume (capitolo o saggio)) (literal)
Anno
  • 2013-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1007/978-3-642-29966-7_15 (literal)
Alternative label
  • Amato G., Falchi F. (2013)
    On kNN classification and local feature based similarity functions
    in Agents and Artificial Intelligence, 2013
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Amato G., Falchi F. (literal)
Pagina inizio
  • 224 (literal)
Pagina fine
  • 239 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#altreInformazioni
  • Progetto: VISTO Tuscany - VIsual Support to Interactive TOurism in Tuscany Grant agreement: D57E09000050007 Third International Conference, ICAART 2011 (Rome, Italy, January, 28-30 2011). Revised Selected Papers (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://link.springer.com/chapter/10.1007%2F978-3-642-29966-7_15 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#titoloVolume
  • Agents and Artificial Intelligence (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#volumeInCollana
  • 271 (literal)
Note
  • PuMa (literal)
  • Scopu (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • CNR-ISTI, Pisa, Italy; CNR-ISTI, Pisa, Italy. (literal)
Titolo
  • On kNN classification and local feature based similarity functions (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn
  • 978-3-642-29965-0 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#curatoriVolume
  • Agents and Artificial Intelligence (literal)
Abstract
  • In this paper we consider the problem of image content recognition and we address it by using local features and kNN based classification strategies. Specifically, we define a number of image similarity functions relying on local features comparing their performance when used with a kNN classifier. Furthermore, we compare the whole image similarity approach with a novel two steps kNN based classification strategy that first assigns a label to each local feature in the document to be classified and then uses this information to assign a label to the whole image. We perform our experiments solving the task of recognizing landmarks in photos. (literal)
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