Bregman divergences for infinite dimensional covariance matrices
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]
|Collections||ANU Research Publications|
|Source:||Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition|
|01_Harandi_Bregman_divergences_for_2014.pdf||284.61 kB||Adobe PDF||Request a copy|
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