Bregman divergences for infinite dimensional covariance matrices

Mehrtash Harandi, Mathieu Salzmann, Fatih Porikli

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

61 Citations (Scopus)


We introduce an approach to computing and comparing Covariance Descriptors (CovDs) in infinite-dimensional spaces. CovDs have become increasingly popular to address classification problems in computer vision. While CovDs offer some robustness to measurement variations, they also throw away part of the information contained in the original data by only retaining the second-order statistics over the measurements. Here, we propose to overcome this limitation by first mapping the original data to a high-dimensional Hilbert space, and only then compute the CovDs. We show that several Bregman divergences can be computed between the resulting CovDs in Hilbert space via the use of kernels. We then exploit these divergences for classification purpose. Our experiments demonstrate the benefits of our approach on several tasks, such as material and texture recognition, person re-identification, and action recognition from motion capture data.

Original languageEnglish
Title of host publicationProceedings - 2014 IEEE Conference on Computer Vision and Pattern Recognition
EditorsRonen Basri, Cornelia Fermuller, Aleix Martinez, René Vidal
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages8
ISBN (Electronic)9781479951178
Publication statusPublished - 2014
Externally publishedYes
EventIEEE Conference on Computer Vision and Pattern Recognition 2014 - Columbus, United States of America
Duration: 23 Jun 201428 Jun 2014 (IEEE Conference Proceedings)

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISSN (Print)1063-6919


ConferenceIEEE Conference on Computer Vision and Pattern Recognition 2014
Abbreviated titleCVPR 2014
Country/TerritoryUnited States of America
Internet address


  • Action Recognition from Motion Capture Data
  • Bregman divergences
  • Covariance Descriptor
  • Material Categorization
  • Reproducing Kernel Hilbert Spaces
  • Riemannian geometry
  • Texture classification
  • Virus Classification

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