Multiscale Decomposition in Low-Rank Approximation

作者：Abdolali Maryam; Rahmati Mohammad^*

来源：IEEE Signal Processing Letters, 2017, 24(7): 1015-1019.

DOI：10.1109/LSP.2017.2704024

• Low-rank approximation
• multiscale representation
• nonlinear optimization
• nuclear norm

In low-rank approximation methods, it is often assumed that the data matrix is composed of two globally low-rank and sparse matrices. Moreover, real data matrices often consist of local patterns in multiple scales. The conventional low-rank approximation techniques do not reveal the local patterns from the data matrices. This letter presents an approach based on decomposition of matrices into low-rank components in different scales. We propose a novel framework using image pyramids comprises of two steps: first locating and then extracting low-rank patterns in multiple scales using nonlinear optimization. Experimentally, we show that the proposed approach is more efficient in extracting low-rank patterns in challenging tasks of illumination normalization in face images and background subtraction in video data.

• 出版日期2017-7