dc.contributor.author Dutta, Aritra dc.contributor.author Li, Xin dc.contributor.author Richtarik, Peter dc.date.accessioned 2018-04-24T06:46:19Z dc.date.available 2018-04-24T06:46:19Z dc.date.issued 2018-04-15 dc.identifier.uri http://hdl.handle.net/10754/627614 dc.description.abstract We 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.publisher arXiv dc.relation.url http://arxiv.org/abs/1804.06252v1 dc.relation.url http://arxiv.org/pdf/1804.06252v1 dc.rights Archived with thanks to arXiv dc.title Weighted Low-Rank Approximation of Matrices and Background Modeling dc.type Preprint dc.contributor.department Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division dc.contributor.department Computer Science Program dc.contributor.department Visual Computing Center (VCC) dc.eprint.version Pre-print dc.contributor.institution Department of Mathematics, University of Central Florida, FL, USA-32816 dc.contributor.institution MIPT dc.contributor.institution University of Edinburgh dc.identifier.arxivid arXiv:1804.06252 kaust.person Dutta, Aritra kaust.person Richtarik, Peter refterms.dateFOA 2018-06-14T04:25:56Z
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