Visually Supporting Image Annotation based on Visual Features and Ontologies

Filali, Jalila; Zghal, Hajer; Martinet, Jean

Type de document :

Autre communication scientifique (congrès sans actes - poster - séminaire...): Communication dans un congrès avec actes

Titre :

Visually Supporting Image Annotation based on Visual Features and Ontologies

Auteur(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]

Titre de la manifestation scientifique :

21st International Conference Information Visualisation

Ville :

London

Pays :

Royaume-Uni

Date de début de la manifestation scientifique :

2017-07-11

Mot(s)-clé(s) en anglais :

Visualisation
image annotation
visual features
ontologies

Discipline(s) HAL :

Informatique [cs]/Vision par ordinateur et reconnaissance de formes [cs.CV]

Résumé en anglais : [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 ...
Lire la suite >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.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Collections :