Beyond Gauss: image-set matching on the Riemannian manifold of PDFs

Mehrtash Harandi, Mathieu Salzmann, Mahsa Baktashmotlagh

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

35 Citations (Scopus)


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 manifolds, 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.

Original languageEnglish
Title of host publicationProceedings - 2015 IEEE International Conference on Computer Vision
EditorsKatsushi Ikeuchi, Christoph Schnörr, Josef Sivic, René Vidal
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages9
ISBN (Electronic)9781467383912
Publication statusPublished - 2015
Externally publishedYes
EventIEEE International Conference on Computer Vision 2015 - Santiago, Chile
Duration: 7 Dec 201513 Dec 2015
Conference number: 15th (Proceedings)


ConferenceIEEE International Conference on Computer Vision 2015
Abbreviated titleICCV 2015
Internet address

Cite this