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Abstract for oa214_BMVC_2005_paper1

In Proc. British Machine Vision Conference, 2005

LEARNING OVER SETS USING BOOSTED MANIFOLD PRINCIPAL ANGLES (BOMPA).

T-K. Kim, O. Arandjelović and R. Cipolla.

2005

In this paper we address the problem of classifying vector sets. We motivate and introduce a novel method based on comparisons between corresponding vector subspaces. In particular, there are two main areas of novelty: (i) we extend the concept of principal angles between linear subspaces to manifolds with arbitrary nonlinearities; (ii) it is demonstrated how boosting can be used for application-optimal principal angle fusion. The strengths of the proposed method are empirically demonstrated on the task of automatic face recognition (AFR), in which it is shown to outperform state-of-the-art methods in the literature.


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© 2005 Cambridge University Engineering Dept
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