ICCV 2017 Tutorial
Covariance 2017
Venice - Sunday October 22
Schedule
- 8:30 Data representation by covariance matrices
- 9:00 Geometry of SPD matrices
- Euclidean distance
- Affine-invariant Riemannian metric
- Log-Euclidean metric
- Bregman divergences
- 9:45 Applications of covariance matrices in computer vision
- 10:15 Morning Break
- 11:00 Data representation by covariance operators
- Positive definite kernels and feature maps
- Covariance operators of feature maps
- 11:30 Geometry of covariance operators
- Hilbert-Schmidt distance
- Affine-invariant Riemannian distance
- Log-Hilbert-Schmidt distance
- Bregman divergences
- 12:15 Applications of covariance operators in computer vision
- Kernel methods with covariance operators
- Two-layer kernel machines with covariance operators
- Comparison with covariance matrices (performance and computational cost)
- Conclusion and future outlook
Slides for Part I: Covariance Matrices and Applications
