3D relevance feedback via multilevel judgements (Articolo in rivista)

Type
Label
  • 3D relevance feedback via multilevel judgements (Articolo in rivista) (literal)
Anno
  • 2010-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1007/s00371-010-0524-0 (literal)
Alternative label
  • D. Giorgi; P. Frosini; M. Spagnuolo; B. Falcidieno (2010)
    3D relevance feedback via multilevel judgements
    in The visual computer; Springer, New York (Stati Uniti d'America)
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • D. Giorgi; P. Frosini; M. Spagnuolo; B. Falcidieno (literal)
Pagina inizio
  • 1321 (literal)
Pagina fine
  • 1338 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#altreInformazioni
  • Paper published in a special issue of six papers that were selected among the presented at the second Eurographics Workshop on 3D Object Retrieval (29th March 2009, Munich, Germany). The papers in the special issue were extended and revised for The Visual Computer journal. (Note: WoS contains imprecise data, as the issue is NOT a proceedings issue, see additional files) (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 26 (literal)
Rivista
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#note
  • Special Issue on 3D Object Retrieval 2009 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroFascicolo
  • 10 (literal)
Note
  • ISI Web of Science (WOS) (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • (1,3,4) IMATI CNR, Genoa, Italy (2) Univ Bologna, ARCES, Fac Engn, Bologna, Italy - Univ Bologna, Dept Math, Bologna, Italy (literal)
Titolo
  • 3D relevance feedback via multilevel judgements (literal)
Abstract
  • Relevance feedback techniques are expected to play an important role in 3D search engines, as they help to bridge the semantic gap between the user and the system. Indeed, similarity is a cognitive process that depends on the observer. We propose a novel relevance feedback technique, which relies on the assumption that similarity may emerge from the inhibition of differences, i.e., from the lack of di- versity with respect to the shape properties taken into ac- count. To this end, a user is provided with a variety of shape descriptors, each analyzing different shape properties. Then the user expresses his/her multilevel relevance judgements, which correspond to his/her concept of similarity among the retrieved objects. Finally, the system inhibits the role of the shape properties that do not reflect the user's idea of simi- larity. The feedback technique is based on a simple scaling procedure, which does not require neither a priori learning nor parameter optimization. We show examples and experi- ments on a benchmark dataset of 3D models. (literal)
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