Ontology and HMAX Features-based Image Classification using Merged Classifiers

Jalila Filali, Hajer Baazaoui Zghal, Jean Martinet

2019

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.

Paper Citation

in Harvard Style

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 (VISIGRAPP 2019) - Volume 5: VISAPP; ISBN 978-989-758-354-4, SciTePress, pages 124-134. DOI: 10.5220/0007444101240134

in Bibtex Style

@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 (VISIGRAPP 2019) - Volume 5: VISAPP},
year={2019},
pages={124-134},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007444101240134},
isbn={978-989-758-354-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - 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
PB - SciTePress