Visually Supporting Image Annotation based ...
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]
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
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 >
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
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