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    Weighted Low-Rank Approximation of Matrices and Background Modeling

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    1804.06252v1.pdf
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    3.666Mb
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    Description:
    Preprint
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    Type
    Preprint
    Authors
    Dutta, Aritra
    Li, Xin cc
    Richtarik, Peter cc
    KAUST Department
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Visual Computing Center (VCC)
    Date
    2018-04-15
    Permanent link to this record
    http://hdl.handle.net/10754/627614
    
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    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.
    Publisher
    arXiv
    arXiv
    1804.06252
    Additional Links
    http://arxiv.org/abs/1804.06252v1
    http://arxiv.org/pdf/1804.06252v1
    Collections
    Preprints; Computer Science Program; Visual Computing Center (VCC); Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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