• English
    • français
  • Help
  •  | 
  • Contact
  •  | 
  • About
  •  | 
  • Login
  • HAL portal
  •  | 
  • Pages Pro
  • EN
  •  / 
  • FR
View Item 
  •   LillOA Home
  • Liste des unités
  • Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
  • View Item
  •   LillOA Home
  • Liste des unités
  • Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Visually Supporting Image Annotation based ...
  • BibTeX
  • CSV
  • Excel
  • RIS

Document type :
Autre communication scientifique (congrès sans actes - poster - séminaire...): Communication dans un congrès avec actes
Title :
Visually Supporting Image Annotation based on Visual Features and Ontologies
Author(s) :
Filali, Jalila [Auteur]
École Nationale des Sciences de l'Informatique [Manouba] [ENSI]
Zghal, Hajer [Auteur]
École Nationale des Sciences de l'Informatique [Manouba] [ENSI]
Martinet, Jean [Auteur]
FOX MIIRE [LIFL]
Conference title :
21st International Conference Information Visualisation
City :
London
Country :
Royaume-Uni
Start date of the conference :
2017-07-11
English keyword(s) :
Visualisation
image annotation
visual features
ontologies
HAL domain(s) :
Informatique [cs]/Vision par ordinateur et reconnaissance de formes [cs.CV]
English abstract : [en]
Automatic Image Annotation (AIA) is a challenging problem in the field of image retrieval, and several methods have been proposed. However, visually supporting this important tasks and reducing the semantic gap between ...
Show more >
Automatic Image Annotation (AIA) is a challenging problem in the field of image retrieval, and several methods have been proposed. However, visually supporting this important tasks and reducing the semantic gap between low-level image features and high-level semantic concepts still remains a key issue. In this paper, we propose a visually supporting image annotation framework based on visual features and ontologies. Our framework relies on three main components: (i) extraction and classification of features component, (ii) ontology's building component and (iii) image annotation component. Our goal consists on improving the visual image annotation by:(1) extracting invariant and complex visual features; (2) integrating feature classification results and semantic concepts to build ontology and (3) combining both visual and semantic similarities during the image annotation process.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
Collections :
  • Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
Source :
Harvested from HAL
Files
Thumbnail
  • https://hal.archives-ouvertes.fr/hal-01693362/document
  • Open access
  • Access the document
Thumbnail
  • https://hal.archives-ouvertes.fr/hal-01693362/document
  • Open access
  • Access the document
Université de Lille

Mentions légales
Université de Lille © 2017