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Beyond Gauss: Image-Set Matching on the Riemannian Manifold of PDFs
2015
2015 IEEE International Conference on Computer Vision (ICCV)
State-of-the-art image-set matching techniques typically implicitly model each image-set with a Gaussian distribution. Here, we propose to go beyond these representations and model image-sets as probability distribution functions (PDFs) using kernel density estimators. To compare and match image-sets, we exploit Csiszár f -divergences, which bear strong connections to the geodesic distance defined on the space of PDFs, i.e., the statistical manifold. Furthermore, we introduce valid positive
doi:10.1109/iccv.2015.468
dblp:conf/iccv/HarandiSB15
fatcat:tsdpeq5lcva4dnlb7lfq5uyvmu