259 citations · 278 across the 10 of their papers we have counts for
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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…
Kernel Coding: General Formulation and Special Cases
Mehrtash Harandi, Mathieu Salzmann
Representing images by compact codes has proven beneficial for many visual recognition tasks. Most existing techniques, however, perform this coding step directly in image feature…
Sparse Coding on Symmetric Positive Definite Manifolds using Bregman Divergences
Mehrtash Harandi, Richard Hartley, Brian Lovell +1
This paper introduces sparse coding and dictionary learning for Symmetric Positive Definite (SPD) matrices, which are often used in machine learning, computer vision and related ar…