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A novel robust principal component analysis method for image and video processing.
- Source :
-
Applications of Mathematics . Apr2016, Vol. 61 Issue 2, p197-214. 18p. - Publication Year :
- 2016
-
Abstract
- The research on the robust principal component analysis has been attracting much attention recently. Generally, the model assumes sparse noise and characterizes the error term by the λ-norm. However, the sparse noise has clustering effect in practice so using a certain λ-norm simply is not appropriate for modeling. In this paper, we propose a novel method based on sparse Bayesian learning principles and Markov random fields. The method is proved to be very effective for low-rank matrix recovery and contiguous outliers detection, by enforcing the low-rank constraint in a matrix factorization formulation and incorporating the contiguity prior as a sparsity constraint. The experiments on both synthetic data and some practical computer vision applications show that the novel method proposed in this paper is competitive when compared with other state-of-the-art methods. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 08627940
- Volume :
- 61
- Issue :
- 2
- Database :
- Academic Search Index
- Journal :
- Applications of Mathematics
- Publication Type :
- Academic Journal
- Accession number :
- 113545152
- Full Text :
- https://doi.org/10.1007/s10492-016-0128-8