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Covariance matrices encoding based on the log-Euclidean and affine invariant Riemannian metrics

Abstract : This paper presents coding methods used to encode a set of covariance matrices. Starting from a Gaussian mixture model adapted to the log-Euclidean or affine invariant Riemannian metric, we propose a Fisher Vector (FV) descriptor adapted to each of these metrics: the log Eu-clidean FV (LE FV) and the Riemannian Fisher Vector (RFV). An experiment is conducted on four conventional texture databases to compare these two metrics and to illustrate the potential of these FV based descriptors compared to state-of-the-art BoW and VLAD based descriptors. A focus is also done to illustrate the advantage of using the Fisher information matrix during the derivation of the FV.
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https://hal.archives-ouvertes.fr/hal-01930136
Contributor : Lionel Bombrun Connect in order to contact the contributor
Submitted on : Wednesday, November 21, 2018 - 4:08:36 PM
Last modification on : Friday, July 30, 2021 - 3:34:03 PM
Long-term archiving on: : Friday, February 22, 2019 - 2:51:14 PM

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Ioana Ilea, Lionel Bombrun, Salem Said, Yannick Berthoumieu. Covariance matrices encoding based on the log-Euclidean and affine invariant Riemannian metrics. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2018, Salt Lake City, United States. ⟨10.1109/CVPRW.2018.00080⟩. ⟨hal-01930136⟩

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