Beyond Gauss: Image-set matching on the Riemannian manifold of PDFs
Harandi, Mehrtash T., Salzmann, Mathieu, & Baktashmotlagh, Mahsa (2015) Beyond Gauss: Image-set matching on the Riemannian manifold of PDFs. In IEEE Conference on Computer Vision (ICVV 2015), 11-18 December 2015, Santiago, Chile.
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 Csiszar´ 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 definite 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.
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|Item Type:||Conference Item (Poster)|
|Divisions:||Current > QUT Faculties and Divisions > Science & Engineering Faculty|
|Copyright Owner:||Copyright 2015 IEEE|
|Deposited On:||29 Mar 2016 02:52|
|Last Modified:||29 Mar 2016 02:55|
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