Kernel analysis over Riemannian manifolds for visual recognition of actions, pedestrians and textures

Mehrtash T. Harandi, Conrad Sanderson, Arnold Wiliem, Brian C. Lovell

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

84 Citations (Scopus)


A convenient way of analysing Riemannian manifolds is to embed them in Euclidean spaces, with the embedding typically obtained by flattening the manifold via tangent spaces. This general approach is not free of drawbacks. For example, only distances between points to the tangent pole are equal to true geodesic distances. This is restrictive and may lead to inaccurate modelling. Instead of using tangent spaces, we propose embedding into the Reproducing Kernel Hilbert Space by introducing a Riemannian pseudo kernel. We furthermore propose to recast a locality preserving projection technique from Euclidean spaces to Riemannian manifolds, in order to demonstrate the benefits of the embedding. Experiments on several visual classification tasks (gesture recognition, person re-identification and texture classification) show that in comparison to tangent-based processing and state-of-the-art methods (such as tensor canonical correlation analysis), the proposed approach obtains considerable improvements in discrimination accuracy.

Original languageEnglish
Title of host publication2012 IEEE Workshop on the Applications of Computer Vision, WACV 2012
Number of pages7
Publication statusPublished - 11 May 2012
Externally publishedYes
EventIEEE Workshop on Applications of Computer Vision 2012 - Breckenridge, United States of America
Duration: 9 Jan 201211 Jan 2012 (Proceedings)

Publication series

NameProceedings of IEEE Workshop on Applications of Computer Vision
ISSN (Print)2158-3978
ISSN (Electronic)2158-3986


ConferenceIEEE Workshop on Applications of Computer Vision 2012
Abbreviated titleWACV 2012
Country/TerritoryUnited States of America
Internet address

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