Enabling content-based image retrieval in very large digital libraries (Contributo in atti di convegno)

Type
Label
  • Enabling content-based image retrieval in very large digital libraries (Contributo in atti di convegno) (literal)
Anno
  • 2009-01-01T00:00:00+01:00 (literal)
Alternative label
  • Lucchese C.; Perego R.; Bolettieri P.; Esuli A.; Falchi F.; Rabitti F. (2009)
    Enabling content-based image retrieval in very large digital libraries
    in Second Workshop on Very Large Digital Libraries, Corfu, Greece, 2 October 2009
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Lucchese C.; Perego R.; Bolettieri P.; Esuli A.; Falchi F.; Rabitti F. (literal)
Pagina inizio
  • 43 (literal)
Pagina fine
  • 50 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#altreInformazioni
  • Gli atti non riportano le pagine degli articoli. (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#note
  • In: VLDL 2009 - Second Workshop on Very Large Digital Libraries (Corfu, Greece, 2 October 2009). Proceedings, pp. 43 - 50. DELOS, 2009. (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#descrizioneSinteticaDelProdotto
  • ABSTRACT: Enabling effective and efficient Content-Based Image Re- trieval (CBIR) on Very Large Digital Libraries (VLDLs), is today an important research issue. While there exist well-known approaches for information retrieval on textual content for VLDLs, the research for an effective CBIR method that is also able to scale to very large collections is still open. A practical effect of this situation is that most of the image retrieval services currently available for VLDLs are based only on tex- tual metadata. In this paper, we report on our experience in creating a collection of 106 million images, i.e., the CoPhIR collection, the largest currently available to the scientific community for research purposes.We discuss the various issues arising from working with a such large col- lection and dealing with a complex retrieval model on information-rich features. We present the non-trivial process of image crawling and de- scriptive feature extraction, using the European EGEE computer GR (literal)
Note
  • Google Scholar (literal)
  • PuMa (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • CNR-ISTI, Pisa (literal)
Titolo
  • Enabling content-based image retrieval in very large digital libraries (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn
  • 9788888506852 (literal)
Abstract
  • Enabling effective and efficient Content-Based Image Re- trieval (CBIR) on Very Large Digital Libraries (VLDLs), is today an important research issue. While there exist well-known approaches for information retrieval on textual content for VLDLs, the research for an effective CBIR method that is also able to scale to very large collections is still open. A practical effect of this situation is that most of the image retrieval services currently available for VLDLs are based only on tex- tual metadata. In this paper, we report on our experience in creating a collection of 106 million images, i.e., the CoPhIR collection, the largest currently available to the scientific community for research purposes.We discuss the various issues arising from working with a such large col- lection and dealing with a complex retrieval model on information-rich features. We present the non-trivial process of image crawling and de- scriptive feature extraction, using the European EGEE computer GRID. The feature extraction phase is often ignored when discussing the scala- bility issue while, as we show in this work, it could be one of the toughest issues to be solved in order to make CBIR feasible on VLDLs (literal)
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