Titre :

Staistical features extraction in wavelet domain for texture classification

Auteur(s) :

Zehani, Soraya [Auteur]
Ouahabi, Abdeldjalil [Auteur]
Imaging, Brain & Neuropsychiatry [iBraiN]
Mimi, Malika [Auteur]
Tahleb Ahmed, Abdelmalik [Auteur]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
COMmunications NUMériques - IEMN [COMNUM - IEMN]

Titre de la manifestation scientifique :

6th International Conference on Image and Signal Processing and their Applications (ISPA 2019)

Ville :

Mostaganem

Pays :

Algérie

Date de début de la manifestation scientifique :

2019-11-24

Éditeur :

IEEE

Mot(s)-clé(s) en anglais :

feature extraction
fractals
image classification
image texture
neural nets
statistical analysis
wavelet transforms

Discipline(s) HAL :

Informatique [cs]
Informatique [cs]/Intelligence artificielle [cs.AI]
Informatique [cs]/Réseaux et télécommunications [cs.NI]
Sciences de l'ingénieur [physics]
Sciences de l'ingénieur [physics]/Traitement du signal et de l'image [eess.SP]
Sciences de l'ingénieur [physics]/Electronique

Résumé en anglais : [en]

This paper presents a new approach for texture classification generalizing a well-known statistical features combining the fractal analysis by means of fractal dimension (FD) with the selection first and second order ...
Lire la suite >This paper presents a new approach for texture classification generalizing a well-known statistical features combining the fractal analysis by means of fractal dimension (FD) with the selection first and second order statistics features in the spatial and wavelet domain. The objective of our paper is to propose the features extraction using statistical parameters in the spatial domain and in wavelet domain with different wavelets, with and without preprocessing stage for the texture classification using neural networks for pattern recognition and studying the effect of the preprocessing and wavelets in classification accuracy. The extracted features are used as the input of the ANN classifier. The performance of the proposed methods are evaluated by using two classes of Brodatz database textures. Finally, classification assessment measures such as the confusion matrix, ROC curves and accuracy are applied to the proposed methods.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Commentaire :

ISBN 978-1-7281-3157-3 e-SIBN 978-1-7281-3156-6

Collections :

• Institut d'Électronique, de Microélectronique et de Nanotechnologie (IEMN) - UMR 8520

Source :

Harvested from HAL