Background subtraction using infinite asymmetric Gaussian mixture models with simultaneous feature selection

Background subtraction using infinite asymmetric Gaussian mixture models with simultaneous feature selection

Mixture models are broadly applied in image processing domains. Related existing challenges include failure to approximate exact data shapes, estimate correct number of components, and ignore irrelevant features. In this study, the authors develop a statistical self‐refinement framework for the back...

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Bibliographic Details
Main Authors: Ziyang Song, Samr Ali, Nizar Bouguila
Format: Article
Language:English
Published: Wiley 2020-09-01
Series:IET Image Processing
Subjects:
variational inference methods
uninformative features
background subtraction task
infinite asymmetric Gaussian mixture models
simultaneous feature selection
image processing domains
Online Access:https://doi.org/10.1049/iet-ipr.2019.1029

Internet

https://doi.org/10.1049/iet-ipr.2019.1029

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