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Optimizing Over Radial Kernels on Compact Manifolds
Sadeep Jayasumana, Richard Hartley, Mathieu Salzmann +2
We tackle the problem of optimizing over all possible positive definite radial kernels on Riemannian manifolds for classification. Kernel methods on Riemannian manifolds have recen…
A Framework for Shape Analysis via Hilbert Space Embedding
Sadeep Jayasumana, Mathieu Salzmann, Hongdong Li +1
We propose a framework for 2D shape analysis using positive definite kernels defined on Kendall's shape manifold. Different representations of 2D shapes are known to generate diffe…
Kernel Methods on the Riemannian Manifold of Symmetric Positive Definite Matrices
Sadeep Jayasumana, Richard Hartley, Mathieu Salzmann +2
Symmetric Positive Definite (SPD) matrices have become popular to encode image information. Accounting for the geometry of the Riemannian manifold of SPD matrices has proven key to…
Kernel Methods on Riemannian Manifolds with Gaussian RBF Kernels
Sadeep Jayasumana, Richard Hartley, Mathieu Salzmann +2
In this paper, we develop an approach to exploiting kernel methods with manifold-valued data. In many computer vision problems, the data can be naturally represented as points on a…
Iteratively Reweighted Graph Cut for Multi-label MRFs with Non-convex Priors
Thalaiyasingam Ajanthan, Richard Hartley, Mathieu Salzmann +1
While widely acknowledged as highly effective in computer vision, multi-label MRFs with non-convex priors are difficult to optimize. To tackle this, we introduce an algorithm that…
Expanding the Family of Grassmannian Kernels: An Embedding Perspective
Mehrtash T. Harandi, Mathieu Salzmann, Sadeep Jayasumana +2
Modeling videos and image-sets as linear subspaces has proven beneficial for many visual recognition tasks. However, it also incurs challenges arising from the fact that linear sub…