Multiscale Decomposition in Low-Rank Approximation
| Autor: | Maryam Abdolali, Mohammad Rahmati |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Normalization (statistics)
Mathematical optimization Background subtraction Applied Mathematics Matrix norm 020206 networking & telecommunications Low-rank approximation 02 engineering and technology Image (mathematics) Nonlinear programming Face (geometry) Signal Processing 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Electrical and Electronic Engineering Algorithm Mathematics Sparse matrix |
| Zdroj: | IEEE Signal Processing Letters. 24:1015-1019 |
| ISSN: | 1558-2361 1070-9908 |
| DOI: | 10.1109/lsp.2017.2704024 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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