A Riemannian Network for SPD Matrix Learning

15 Aug 2016Zhiwu HuangLuc Van Gool

Symmetric Positive Definite (SPD) matrix learning methods have become popular in many image and video processing tasks, thanks to their ability to learn appropriate statistical representations while respecting Riemannian geometry of underlying SPD manifolds. In this paper we build a Riemannian network architecture to open up a new direction of SPD matrix non-linear learning in a deep model... (read more)

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