Relational divergence based classification on Riemannian manifolds

Alavi, Azadeh, Harandi, Mehrtash and Sanderson, Conrad (2013). Relational divergence based classification on Riemannian manifolds. In: Proceedings of IEEE Workshop on Applications of Computer Vision. 2013 IEEE Workshop on Applications of Computer Vision, Tampa, FL, United States, (111-116). 15-17 January 2013. doi:10.1109/WACV.2013.6475007

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Author Alavi, Azadeh
Harandi, Mehrtash
Sanderson, Conrad
Title of paper Relational divergence based classification on Riemannian manifolds
Conference name 2013 IEEE Workshop on Applications of Computer Vision
Conference location Tampa, FL, United States
Conference dates 15-17 January 2013
Proceedings title Proceedings of IEEE Workshop on Applications of Computer Vision   Check publisher's open access policy
Journal name Proceedings of IEEE Workshop on Applications of Computer Vision   Check publisher's open access policy
Place of Publication Piscataway, NJ, United States
Publisher IEEE (Institute for Electrical and Electronic Engineers)
Publication Year 2013
Sub-type Fully published paper
DOI 10.1109/WACV.2013.6475007
ISBN 9781467350525
9781467350532
ISSN 1550-5790
Start page 111
End page 116
Total pages 6
Collection year 2014
Language eng
Abstract/Summary A recent trend in computer vision is to represent images through covariance matrices, which can be treated as points on a special class of Riemannian manifolds. A popular way of analysing such manifolds is to embed them in Euclidean spaces, a process which can be interpreted as warping the feature space. Embedding manifolds is not without problems, as the manifold structure may not be accurately preserved. In this paper, we propose a new method for analysing Riemannian manifolds, where embedding into Euclidean spaces is not explicitly required. To this end, we propose to represent Riemannian points through their similarities to a set of reference points on the manifold, with the aid of the recently proposed Stein divergence, which is a symmetrised version of Bregman matrix divergence. Classification problems on manifolds are then effectively converted into the problem of finding appropriate machinery over the space of similarities, which can be tackled by conventional Euclidean learning methods such as linear discriminant analysis. Experiments on face recognition, person re-identification and texture classification show that the proposed method outperforms state-of-the-art approaches, such as Tensor Sparse Coding, Histogram Plus Epitome and the recent Riemannian Locality Preserving Projection.
Q-Index Code E1
Q-Index Status Confirmed Code
Institutional Status UQ

 
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Created: Sun, 23 Jun 2013, 03:28:29 EST by Conrad Sanderson on behalf of School of Information Technol and Elec Engineering