Riemannian covariance descriptors(RieCovDs) via covariance computation on the manifold of Gaussians for image set coding. Written by Kai-Xuan Chen (e-mail: chenkx.jsh@aliyun.com, chenkx@zju.edu.cn)

The ETH-80 dataset is needed to be downloaded(https://github.com/Kai-Xuan/ETH-80/),
and put 8 filefolders(visual image sets from 8 different categories) into filefolder '.\ETH-80'.
Please run 'read_ETH.m' to generate RieCovDs. Then run 'run_ETH_NNMethods.m' and 'run_ETH_DisMethods.m' for image set classification.

If you find this repository useful for your research, Please cite the following paper:
BibTex :

@article{chen2020covariance,
  title={Covariance Descriptors on a Gaussian Manifold and their Application to Image Set Classification},
  author={Chen, Kai-Xuan and Ren, Jie-Yi and Wu, Xiao-Jun and Kittler, Josef},
  journal={Pattern Recognition},
  pages={107463},
  year={2020},
  publisher={Elsevier}
}

For more experiment, you can test on Virus dataset (https://github.com/Kai-Xuan/Virus/)

For more technical details.

1. Distances on the SPD manifold: https://github.com/Kai-Xuan/SPD-OPERATIONS/tree/master/SPD-Metrics/
2. Means on the SPD manifold: https://github.com/Kai-Xuan/SPD-OPERATIONS/tree/master/SPD-Means/
3. Local Difference Vectors on the SPD manifold: https://github.com/Kai-Xuan/SPD-OPERATIONS/tree/master/SPD-LDV/