Sparse Representation-based Classification
6 papers with code • 1 benchmarks • 1 datasets
Limited annotated data available for the recognition of facial expression and action units embarrasses the training of deep networks, which can learn disentangled invariant features.
In this paper, we design a Collaborative-Hierarchical Sparse and Low-Rank (C-HiSLR) model that is natural for recognizing human emotion in visual data.
Using low-frequency (UHF to L-band) ultra-wideband (UWB) synthetic aperture radar (SAR) technology for detecting buried and obscured targets, e. g. bomb or mine, has been successfully demonstrated recently.
Multiplication fusion of sparse and collaborative-competitive representation for image classification
Firstly, the coefficients of the test sample are obtained by SRC and CCRC, respectively.