Weighted Low-Rank Approximation of Matrices and Background Modeling
Type
PreprintAuthors
Dutta, AritraLi, Xin

Richtarik, Peter

KAUST Department
Computer Science ProgramComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Visual Computing Center (VCC)
Date
2018-04-15Permanent link to this record
http://hdl.handle.net/10754/627614
Metadata
Show full item recordAbstract
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.Publisher
arXivarXiv
arXiv:1804.06252Additional Links
http://arxiv.org/abs/1804.06252v1http://arxiv.org/pdf/1804.06252v1