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Authors: Olfa Allani 1 ; Nedra Mellouli 2 ; Hajer Baazaoui Zghal 3 ; Herman Akdag 4 and Henda Ben Ghzala 3

Affiliations: 1 Université Paris 8 and LABORATOIRE RIADI, France ; 2 Universite Paris 8 Saint-Denis, France ; 3 LABORATOIRE RIADI, Tunisia ; 4 Universite Paris 8, France

Keyword(s): Visual Feature, Ontologies, Semantic Content, Selection.

Related Ontology Subjects/Areas/Topics: Computer Vision, Visualization and Computer Graphics ; Features Extraction ; Image and Video Analysis

Abstract: Content-Based Image Retrieval approaches have been marked by the semantic gap (inconsistency) between the perception of the user and the visual description of the image. This inconsistency is often linked to the use of predefined visual features randomly selected and applied whatever the application domain. In this paper we propose an approach that adapts the selection of visual features to semantic content ensuring the coherence between them. We first design visual and semantic descriptive ontologies. These ontologies are then explored by association rules aiming to link semantic descriptor (a concept) to a set of visual features. The obtained feature collections are selected according to the annotated query images. Different strategies have been experimented and their results have shown an improvement of the retrieval task based on relevant feature selections.

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Paper citation in several formats:
Allani, O.; Mellouli, N.; Baazaoui Zghal, H.; Akdag, H. and Ghzala, H. (2015). A Relevant Visual Feature Selection Approach for Image Retrieval. In Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2015) ISBN 978-989-758-090-1; ISSN 2184-4321, pages 377-384. DOI: 10.5220/0005306303770384

@conference{visapp15,
author={Olfa Allani. and Nedra Mellouli. and Hajer {Baazaoui Zghal}. and Herman Akdag. and Henda Ben Ghzala.},
title={A Relevant Visual Feature Selection Approach for Image Retrieval},
booktitle={Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2015)},
year={2015},
pages={377-384},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005306303770384},
isbn={978-989-758-090-1},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2015)
TI - A Relevant Visual Feature Selection Approach for Image Retrieval
SN - 978-989-758-090-1
IS - 2184-4321
AU - Allani, O.
AU - Mellouli, N.
AU - Baazaoui Zghal, H.
AU - Akdag, H.
AU - Ghzala, H.
PY - 2015
SP - 377
EP - 384
DO - 10.5220/0005306303770384