A novel robust principal component analysis method for image and video processing

作者：Huan, Guoqiang; Li, Ying^*; Song, Zhanjie

来源：Applications of Mathematics, 2016, 61(2): 197-214.

DOI：10.1007/s10492-016-0128-8

• robust principal component analysis
• sparse Bayesian learning
• Markov random fields
• matrix factorization
• contiguity prior

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 lambda(1)-norm. However, the sparse noise has clustering effect in practice so using a certain lambda (p) -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.

• 出版日期2016-4
• 单位天津大学