Fast Convolutional Sparse Coding in the Dual Domain

Handle URI:
http://hdl.handle.net/10754/626494
Title:
Fast Convolutional Sparse Coding in the Dual Domain
Authors:
Affara, Lama Ahmed; Ghanem, Bernard ( 0000-0002-5534-587X ) ; Wonka, Peter ( 0000-0003-0627-9746 )
Abstract:
Convolutional sparse coding (CSC) is an important building block of many computer vision applications ranging from image and video compression to deep learning. We present two contributions to the state of the art in CSC. First, we significantly speed up the computation by proposing a new optimization framework that tackles the problem in the dual domain. Second, we extend the original formulation to higher dimensions in order to process a wider range of inputs, such as color inputs, or HOG features. Our results show a significant speedup compared to the current state of the art in CSC.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Computer Science Program; Electrical Engineering Program; Visual Computing Center (VCC)
Publisher:
arXiv
Issue Date:
27-Sep-2017
ARXIV:
arXiv:1709.09479
Type:
Preprint
Additional Links:
http://arxiv.org/abs/1709.09479v1; http://arxiv.org/pdf/1709.09479v1
Appears in Collections:
Other/General Submission; Other/General Submission; Computer Science Program; Electrical Engineering Program; Visual Computing Center (VCC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorAffara, Lama Ahmeden
dc.contributor.authorGhanem, Bernarden
dc.contributor.authorWonka, Peteren
dc.date.accessioned2017-12-28T07:32:13Z-
dc.date.available2017-12-28T07:32:13Z-
dc.date.issued2017-09-27en
dc.identifier.urihttp://hdl.handle.net/10754/626494-
dc.description.abstractConvolutional sparse coding (CSC) is an important building block of many computer vision applications ranging from image and video compression to deep learning. We present two contributions to the state of the art in CSC. First, we significantly speed up the computation by proposing a new optimization framework that tackles the problem in the dual domain. Second, we extend the original formulation to higher dimensions in order to process a wider range of inputs, such as color inputs, or HOG features. Our results show a significant speedup compared to the current state of the art in CSC.en
dc.publisherarXiven
dc.relation.urlhttp://arxiv.org/abs/1709.09479v1en
dc.relation.urlhttp://arxiv.org/pdf/1709.09479v1en
dc.rightsArchived with thanks to arXiven
dc.titleFast Convolutional Sparse Coding in the Dual Domainen
dc.typePreprinten
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentComputer Science Programen
dc.contributor.departmentElectrical Engineering Programen
dc.contributor.departmentVisual Computing Center (VCC)en
dc.eprint.versionPre-printen
dc.identifier.arxividarXiv:1709.09479en
kaust.authorAffara, Lama Ahmeden
kaust.authorGhanem, Bernarden
kaust.authorWonka, Peteren
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