Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloVisual Recognition of Ancient Inscriptions Using Convolutional Neural Network and Fisher Vector
Anno di pubblicazione2016
FormatoElettronico
Autore/iAmato G.; Falchi F.; Vadicamo L.
Affiliazioni autoriCNR-ISTI, Pisa, Italy; CNR-ISTI, Pisa, Italy; CNR-ISTI, Pisa, Italy
Autori CNR e affiliazioni
  • LUCIA VADICAMO
  • GIUSEPPE AMATO
  • FABRIZIO FALCHI
Lingua/e
  • inglese
AbstractBy bringing together the most prominent European institutions and archives in the field of Classical Latin and Greek epigraphy, the EAGLE project has collected the vast majority of the surviving Greco-Latin inscriptions into a single readily-searchable database. Text-based search engines are typically used to retrieve information about ancient inscriptions (or about other artifacts). These systems require that the users formulate a text query that contains information such as the place where the object was found or where it is currently located. Conversely, visual search systems can be used to provide information to users (like tourists and scholars) in a most intuitive and immediate way, just using an image as query. In this article, we provide a comparison of several approaches for visual recognizing ancient inscriptions. Our experiments, conducted on 17, 155 photos related to 14, 560 inscriptions, show that BoW and VLAD are outperformed by both Fisher Vector (FV) and Convolutional Neural Network (CNN) features. More interestingly, combining FV and CNN features into a single image representation allows achieving very high effectiveness by correctly recognizing the query inscription in more than 90% of the cases. Our results suggest that combinations of FV and CNN can be also exploited to effectively perform visual retrieval of other types of objects related to cultural heritage such as landmarks and monuments.
Lingua abstractinglese
Altro abstract-
Lingua altro abstract-
Pagine da21
Pagine a24
Pagine totali-
RivistaACM journal on computing and cultural heritage (Print)
Attiva dal 2008
Editore: Association for Computing Machinery - New York, NY
Paese di pubblicazione: Stati Uniti d'America
Lingua: inglese
ISSN: 1556-4673
Titolo chiave: ACM journal on computing and cultural heritage (Print)
Titolo proprio: ACM journal on computing and cultural heritage. (Print)
Titolo abbreviato: ACM j. comput. cult. herit. (Print)
Titoli alternativi:
  • Journal on computing and cultural heritage
  • JOCCH
Numero volume della rivista9
Fascicolo della rivista4
DOI10.1145/2964911
Verificato da refereeSì: Internazionale
Stato della pubblicazionePublished version
Indicizzazione (in banche dati controllate)
  • ISI Web of Science (WOS) (Codice:000391726300004)
  • Scopus (Codice:2-s2.0-85006974335)
Parole chiaveFisher vector, convolutional neural network, epigraphy, Latin and Greek inscriptions
Link (URL, URI)http://dl.acm.org/citation.cfm?id=2964911
Titolo parallelo-
Licenza-
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Strutture CNR
  • ISTI — Istituto di scienza e tecnologie dell'informazione "Alessandro Faedo"
Moduli/Attività/Sottoprogetti CNR
  • ICT.P08.010.002 : Digital Libraries
Progetti Europei
Allegati
Visual Recognition of Ancient Inscriptions Using Convolutional Neural Network and Fisher Vector (documento privato )
Descrizione: Codice PuMa: cnr.isti/2016-A0-087
Tipo documento: application/pdf