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

Harandi, Mehrtash T., Sanderson, Conrad, Wiliem, Arnold and Lovell, Brian C. (2012). Kernel analysis over Riemannian manifolds for visual recognition of actions, pedestrians and textures. In: Proceedings of IEEE Workshop on Applications of Computer Vision. 2012 IEEE Workshop on Applications of Computer Vision, Breckenridge, CO, United States, (433-439). 9-11 January 2012. doi:10.1109/WACV.2012.6163005

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Author Harandi, Mehrtash T.
Sanderson, Conrad
Wiliem, Arnold
Lovell, Brian C.
Title of paper Kernel analysis over Riemannian manifolds for visual recognition of actions, pedestrians and textures
Language of Title eng
Conference name 2012 IEEE Workshop on Applications of Computer Vision
Conference location Breckenridge, CO, United States
Conference dates 9-11 January 2012
Proceedings title Proceedings of IEEE Workshop on Applications of Computer Vision
Language of Proceedings Title eng
Journal name Proceedings of IEEE Workshop on Applications of Computer Vision
Language of Journal Name eng
Place of Publication Piscataway, NJ, United States
Publisher IEEE (Institute for Electrical and Electronic Engineers)
Publication Year 2012
Sub-type Fully published paper
DOI 10.1109/WACV.2012.6163005
ISBN 9781467302333
ISSN 2158-3978
Start page 433
End page 439
Total pages 7
Collection year 2013
Language eng
Formatted Abstract/Summary
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 tangentbased processing and state-of-the-art methods (such as tensor canonical correlation analysis), the proposed approach obtains considerable improvements in discrimination accuracy.
Q-Index Code E1
Q-Index Status Confirmed Code
Institutional Status UQ

 
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Created: Tue, 06 Mar 2012, 23:13:09 EST by Dr Mehrtash Harandi on behalf of School of Information Technol and Elec Engineering