2015 IEEE International Conference on Computer Vision (ICCV) (2015)
Dec. 7, 2015 to Dec. 13, 2015
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICCV.2015.39
In this paper, we propose a new sparsity regularizer for measuring the low-rank structure underneath a tensor. The proposed sparsity measure has a natural physical meaning which is intrinsically the size of the fundamental Kronecker basis to express the tensor. By embedding the sparsity measure into the tensor completion and tensor robust PCA frameworks, we formulate new models to enhance their capability in tensor recovery. Through introducing relaxation forms of the proposed sparsity measure, we also adopt the alternating direction method of multipliers (ADMM) for solving the proposed models. Experiments implemented on synthetic and multispectral image data sets substantiate the effectiveness of the proposed methods.
Tensile stress, Robustness, Principal component analysis, Computer vision, Current measurement, Computational modeling, Brain modeling
Q. Zhao et al., "A Novel Sparsity Measure for Tensor Recovery," 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile, 2015, pp. 271-279.