A Relevant Visual Feature Selection Approach for Image Retrieval

Olfa Allani, Nedra Mellouli, Hajer Baazaoui Zghal, Herman Akdag, Henda Ben Ghzala

2015

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.

Paper Citation

in Harvard Style

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 2: VISAPP, (VISIGRAPP 2015) ISBN 978-989-758-090-1, pages 377-384. DOI: 10.5220/0005306303770384

in Bibtex Style

@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 2: VISAPP, (VISIGRAPP 2015)},
year={2015},
pages={377-384},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005306303770384},
isbn={978-989-758-090-1},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2015)
TI - A Relevant Visual Feature Selection Approach for Image Retrieval
SN - 978-989-758-090-1
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