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Bregman divergences for infinite dimensional covariance matrices

Harandi, Mehrtash; Salzmann, Mathieu; Porikli, Fatih

Description

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...[Show more]

dc.contributor.authorHarandi, Mehrtash
dc.contributor.authorSalzmann, Mathieu
dc.contributor.authorPorikli, Fatih
dc.coverage.spatialColumbus USA
dc.date.accessioned2015-12-10T22:23:20Z
dc.date.createdJune 23-28 2014
dc.identifier.isbn9781479951178
dc.identifier.urihttp://hdl.handle.net/1885/52731
dc.description.abstractWe 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.
dc.publisherIEEE
dc.relation.ispartofseries27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014
dc.sourceProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
dc.titleBregman divergences for infinite dimensional covariance matrices
dc.typeConference paper
local.description.notesImported from ARIES
local.description.refereedYes
dc.date.issued2014
local.identifier.absfor080104 - Computer Vision
local.identifier.ariespublicationa383154xPUB254
local.type.statusPublished Version
local.contributor.affiliationHarandi, Mehrtash, College of Engineering and Computer Science, ANU
local.contributor.affiliationSalzmann, Mathieu, College of Engineering and Computer Science, ANU
local.contributor.affiliationPorikli, Fatih, College of Engineering and Computer Science, ANU
local.description.embargo2037-12-31
local.bibliographicCitation.startpage1003
local.bibliographicCitation.lastpage1010
local.identifier.doi10.1109/CVPR.2014.132
dc.date.updated2015-12-09T09:06:46Z
local.identifier.scopusID2-s2.0-84911424665
CollectionsANU Research Publications

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