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2008
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Learning a geometry integrated image appearance manifold from a small training set

9 years 6 months ago
Learning a geometry integrated image appearance manifold from a small training set
While low-dimensional image representations have been very popular in computer vision, they suffer from two limitations: (i) they require collecting a large and varied training set to learn a low-dimensional set of basis functions, and (ii) they do not retain information about the 3D geometry of the object being imaged. In this paper, we show that it is possible to estimate low-dimensional manifolds that describe object appearance while retaining the geometrical information about the 3D structure of the object. By using a combination of analytically derived geometrical models and statistical learning methods, this can be achieved using a much smaller training set than most of the existing approaches. Specifically, we derive a quadrilinear manifold of object appearance that can represent the effects of illumination, pose, identity and deformation, and the basis functions of the tangent space to this manifold depend on the 3D surface normals of the objects. We show experimental results...
Yilei Xu, Amit K. Roy Chowdhury
Added 29 May 2010
Updated 29 May 2010
Type Conference
Year 2008
Where CVPR
Authors Yilei Xu, Amit K. Roy Chowdhury
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