Feature selection based on discriminative power under uncertainty for computer vision applications
Chakroun, Marwa (University of Sfax. Control and Energy Management Lab)
Bouhamed, Sonda Ammar (University of Sfax. Control and Energy Management Lab)
Kallel, Imene Khanfir (University of Sfax. Control and Energy Management Lab)
Solaiman, Basel (IMT Atlantique (Brest, França))
Derbel, Houda (University of Sfax. Control and Energy Management Lab)

Date:	2022
Abstract:	Feature selection is a prolific research field, which has been widely studied in the last decades and has been successfully applied to numerous computer vision systems. It mainly aims to reduce the dimensionality and thus the system complexity. Features have not the same importance within the different classes. Some of them perform for class representation while others perform for class separation. In this paper, a new feature selection method based on discriminative power is proposed to select the relevant features under an uncertain framework, where the uncertainty is expressed through a possibility distribution. In an uncertain context, our method shows its ability to select features that can represent and discriminate between classes.
Rights:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.
Language:	Anglès
Document:	Article ; Versió publicada
Subject:	Computer vision ; Features and image descriptors ; Machine learning and data mining ; Image analysis and processing ; Applications
Published in:	ELCVIA : Electronic Letters on Computer Vision and Image Analysis, Vol. 21 Núm. 1 (2022) , p. 111-120 (Regular Issue) , ISSN 1577-5097

Adreça original: https://elcvia.cvc.uab.cat/article/view/1361
DOI: 10.5565/rev/elcvia.1361

10 p, 2.2 MB

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