Weighted Low Rank Approximation For Background Estimation Problems

Abstract

Classical principal component analysis (PCA) is not robust when the data contain sparse outliers. The use of the ℓ1 norm in the Robust PCA (RPCA) method successfully eliminates this weakness of PCA in separating the sparse outliers. Here we propose a weighted low rank (WLR) method, where a simple weight is inserted inside the Frobenius norm. We demonstrate how this method tackles often computationally expensive algorithms that rely on the ℓ1 norm. As a proof of concept, we present a background estimation model based on WLR, and we compare the model with RPCA method and with other state-of-the-art algorithms used for background estimation. Our empirical validation shows that the weighted low-rank approximation we propose here can perform as well as or better than that of RPCA and other state-of-the-art algorithms.

Publication Date

7-1-2017

Publication Title

Proceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017

Volume

2018-January

Number of Pages

1853-1861

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/ICCVW.2017.219

Socpus ID

85046268328 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85046268328

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