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Geodesic finite mixture models

AuthorsSimo-Serra, Edgar CSIC; Torras, Carme CSIC ORCID ; Moreno-Noguer, Francesc CSIC ORCID
Issue Date2014
Citation25th British Machine Vision Conference (2014)
AbstractWe present a novel approach for learning a finite mixture model on a Riemannian manifold in which Euclidean metrics are not applicable and one needs to resort to geodesic distances consistent with the manifold geometry. For this purpose, we draw inspiration on a variant of the expectation-maximization algorithm, that uses a minimum message length criterion to automatically estimate the optimal number of components from multivariate data lying on an Euclidean space. In order to use this approach on Riemannian manifolds, we propose a formulation in which each component is defined on a different tangent space, thus avoiding the problems associated with the loss of accuracy produced when linearizing the manifold with a single tangent space. Our approach can be applied to any type of manifold for which it is possible to estimate its tangent space. In particular, we show results on synthetic examples of a sphere and a quadric surface and on a large and complex dataset of human poses, where the proposed model is used as a regression tool for hypothesizing the geometry of occluded parts of the body.
DescriptionTrabajo presentado a la 25th British Machine Vision Conference (BMVC), celebrada en Nottingham (UK) del 1 al 5 de septiembre de 2014.-- Este ítem (excepto textos e imágenes no creados por el autor) está sujeto a una licencia de Creative Commons: Attribution-NonCommercial-NoDerivs 3.0 Spain.
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Appears in Collections:(IRII) Comunicaciones congresos

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