Ontology and HMAX Features-based Image Classification using Merged Classifiers

Filali, Jalila; Zghal, Hajer; Martinet, Jean

Type de document :

Autre communication scientifique (congrès sans actes - poster - séminaire...): Communication dans un congrès avec actes

Titre :

Ontology and HMAX Features-based Image Classification using Merged Classifiers

Auteur(s) :

Filali, Jalila [Auteur]
École Nationale des Sciences de l'Informatique [Manouba] [ENSI]
Zghal, Hajer [Auteur]
École Nationale des Sciences de l'Informatique [Manouba] [ENSI]
Martinet, Jean [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]

Titre de la manifestation scientifique :

International Conference on Computer Vision Theory and Applications 2019 (VISAPP'19)

Ville :

Prague

Pays :

République tchèque

Date de début de la manifestation scientifique :

2019-02-25

Mot(s)-clé(s) en anglais :

Image Classification
HMAX Features
Ontology

Discipline(s) HAL :

Informatique [cs]/Vision par ordinateur et reconnaissance de formes [cs.CV]

Résumé en anglais : [en]

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 ...
Lire la suite >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 onto-logies 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.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Collections :

Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL

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