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dc.contributor.authorBibi, Adel
dc.contributor.authorGhanem, Bernard
dc.date.accessioned2018-03-11T06:54:12Z
dc.date.available2018-03-11T06:54:12Z
dc.date.issued2017-12-25
dc.identifier.citationBibi A, Ghanem B (2017) High Order Tensor Formulation for Convolutional Sparse Coding. 2017 IEEE International Conference on Computer Vision (ICCV). Available: http://dx.doi.org/10.1109/ICCV.2017.197.
dc.identifier.doi10.1109/ICCV.2017.197
dc.identifier.urihttp://hdl.handle.net/10754/627257
dc.description.abstractConvolutional sparse coding (CSC) has gained attention for its successful role as a reconstruction and a classification tool in the computer vision and machine learning community. Current CSC methods can only reconstruct singlefeature 2D images independently. However, learning multidimensional dictionaries and sparse codes for the reconstruction of multi-dimensional data is very important, as it examines correlations among all the data jointly. This provides more capacity for the learned dictionaries to better reconstruct data. In this paper, we propose a generic and novel formulation for the CSC problem that can handle an arbitrary order tensor of data. Backed with experimental results, our proposed formulation can not only tackle applications that are not possible with standard CSC solvers, including colored video reconstruction (5D- tensors), but it also performs favorably in reconstruction with much fewer parameters as compared to naive extensions of standard CSC to multiple features/channels.
dc.description.sponsorshipThis work was supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttp://ieeexplore.ieee.org/document/8237459/
dc.titleHigh Order Tensor Formulation for Convolutional Sparse Coding
dc.typeConference Paper
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentElectrical Engineering Program
dc.contributor.departmentVisual Computing Center (VCC)
dc.identifier.journal2017 IEEE International Conference on Computer Vision (ICCV)
dc.conference.date2017-10-22 to 2017-10-29
dc.conference.name16th IEEE International Conference on Computer Vision, ICCV 2017
dc.conference.locationVenice, ITA
kaust.personBibi, Adel Aamer
kaust.personGhanem, Bernard
dc.date.published-online2017-12-25
dc.date.published-print2017-10


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