Beyond Gauss: Image-Set Matching on the Riemannian Manifold of PDFs

Mehrtash Harandi, Mathieu Salzmann, Mahsa Baktashmotlagh
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
more » ... nite kernels on the statistical manifold, which let us make use of more powerful classification schemes to match image-sets. Finally, we introduce a supervised dimensionality reduction technique that learns a latent space where f -divergences reflect the class labels of the data. Our experiments on diverse problems, such as video-based face recognition and dynamic texture classification, evidence the benefits of our approach over the state-of-the-art image-set matching methods.
doi:10.1109/iccv.2015.468 dblp:conf/iccv/HarandiSB15 fatcat:tsdpeq5lcva4dnlb7lfq5uyvmu