Bregman divergences for infinite dimensional covariance matrices

Mehrtash Harandi*, Mathieu Salzmann, Fatih Porikli

*Corresponding author for this work

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

    81 Citations (Scopus)

    Abstract

    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 of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
    PublisherIEEE Computer Society
    Pages1003-1010
    Number of pages8
    ISBN (Electronic)9781479951178, 9781479951178
    DOIs
    Publication statusPublished - 24 Sept 2014
    Event27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014 - Columbus, United States
    Duration: 23 Jun 201428 Jun 2014

    Publication series

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

    Conference

    Conference27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014
    Country/TerritoryUnited States
    CityColumbus
    Period23/06/1428/06/14

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