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Authors: Jalila Filali 1 ; Hajer Zghal 1 and Jean Martinet 2

Affiliations: 1 ENSI, RIADI Laboratory, University of Manouba, Tunisia ; 2 Univ. Lille, CNRS, Centrale Lille, UMR 9189 – CRIStAL – Centre de Recherche en Informatique, Signal et Automatique de Lille, F-59000, Lille, France

ISBN: 978-989-758-354-4

Keyword(s): Image Classification, HMAX Features, Ontology.

Abstract: Bag-of-Viusal-Words (BoVW) model has been widely used in the area of image classification, which rely on building visual vocabulary. Recently, attention has been shifted to the use of advanced architectures which are characterized by multilevel processing. HMAX model (Hierarchical Max-pooling model) has attracted a great deal of attention in image classification. Recent works, in image classification, consider the integration of ontologies and semantic structures is useful to improve image classification. In this paper, we propose an approach of image classification based on ontology and HMAX features using merged classifiers. Our contribution resides in exploiting ontological relationships between image categories in line with training visual-feature classifiers, and by merging the outputs of hypernym-hyponym classifiers to lead to a better discrimination between classes. Our purpose is to improve image classification by using ontologies. Several strategies have been experimented and the obtained results have shown that our proposal improves image classification. Results based our ontology outperform results obtained by baseline methods without ontology. Moreover, the deep learning network Inception-v3 is experimented and compared with our method, classification results obtained by our method outperform Inception-v3 for some image classes. (More)

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Paper citation in several formats:
Filali, J.; Zghal, H. and Martinet, J. (2019). Ontology and HMAX Features-based Image Classification using Merged Classifiers.In Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, ISBN 978-989-758-354-4, pages 124-134. DOI: 10.5220/0007444101240134

@conference{visapp19,
author={Jalila Filali. and Hajer Baazaoui Zghal. and Jean Martinet.},
title={Ontology and HMAX Features-based Image Classification using Merged Classifiers},
booktitle={Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP,},
year={2019},
pages={124-134},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007444101240134},
isbn={978-989-758-354-4},
}

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP,
TI - Ontology and HMAX Features-based Image Classification using Merged Classifiers
SN - 978-989-758-354-4
AU - Filali, J.
AU - Zghal, H.
AU - Martinet, J.
PY - 2019
SP - 124
EP - 134
DO - 10.5220/0007444101240134

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