Visually Supporting Image Annotation based on Visual Features and Ontologies

Jalila Filali (1) , Hajer Baazaoui Zghal (1) , Jean Martinet (2)

1 ENSI - École Nationale des Sciences de l'Informatique [Manouba]
2 LIFL - FOX MIIRE

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.

Mots clés: Visualisation; image annotation; visual features; ontologies

Domaines: Vision par ordinateur et reconnaissance de formes [cs.CV]

Jalila Filali, Hajer Baazaoui Zghal, Jean Martinet. Visually Supporting Image Annotation based on Visual Features and Ontologies. 21st International Conference Information Visualisation, Jul 2017, London, United Kingdom. ⟨hal-01693362⟩