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dc.contributor.authorDutta, Aritra
dc.contributor.authorLi, Xin
dc.contributor.authorRichtarik, Peter
dc.date.accessioned2018-04-24T06:46:19Z
dc.date.available2018-04-24T06:46:19Z
dc.date.issued2018-04-15
dc.identifier.urihttp://hdl.handle.net/10754/627614
dc.description.abstractWe primarily study a special a weighted low-rank approximation of matrices and then apply it to solve the background modeling problem. We propose two algorithms for this purpose: one operates in the batch mode on the entire data and the other one operates in the batch-incremental mode on the data and naturally captures more background variations and computationally more effective. Moreover, we propose a robust technique that learns the background frame indices from the data and does not require any training frames. We demonstrate through extensive experiments that by inserting a simple weight in the Frobenius norm, it can be made robust to the outliers similar to the $\ell_1$ norm. Our methods match or outperform several state-of-the-art online and batch background modeling methods in virtually all quantitative and qualitative measures.
dc.publisherarXiv
dc.relation.urlhttp://arxiv.org/abs/1804.06252v1
dc.relation.urlhttp://arxiv.org/pdf/1804.06252v1
dc.rightsArchived with thanks to arXiv
dc.titleWeighted Low-Rank Approximation of Matrices and Background Modeling
dc.typePreprint
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentComputer Science Program
dc.contributor.departmentVisual Computing Center (VCC)
dc.eprint.versionPre-print
dc.contributor.institutionDepartment of Mathematics, University of Central Florida, FL, USA-32816
dc.contributor.institutionMIPT
dc.contributor.institutionUniversity of Edinburgh
dc.identifier.arxividarXiv:1804.06252
kaust.personDutta, Aritra
kaust.personRichtarik, Peter
refterms.dateFOA2018-06-14T04:25:56Z


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