Covariances in Computer Vision and Machine Learning

DOI10.2200/S00801ED1V01Y201709COV011zbMath1380.68005OpenAlexW2767669108MaRDI QIDQ4598383

Ha Quang Minh, Vittorio Murino

Publication date: 21 December 2017

Published in: Synthesis Lectures on Computer Vision (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.2200/s00801ed1v01y201709cov011

zbMATH Keywords

kernel methodscovariance operatorsdata representation by covariance matricesgeometry of covariance operatorsgeometry of SPD matrices

Mathematics Subject Classification ID

Image analysis in multivariate analysis (62H35) Learning and adaptive systems in artificial intelligence (68T05) Positive matrices and their generalizations; cones of matrices (15B48) Research exposition (monographs, survey articles) pertaining to computer science (68-02) Machine vision and scene understanding (68T45) Methods of local Riemannian geometry (53B21) Linear operators in reproducing-kernel Hilbert spaces (including de Branges, de Branges-Rovnyak, and other structured spaces) (47B32)

Related Items (5)

Diffusion tensor regularization with metric double integrals ⋮ Entropic regularization of Wasserstein distance between infinite-dimensional Gaussian measures and Gaussian processes ⋮ Alpha-beta log-determinant divergences between positive definite trace class operators ⋮ Regularized divergences between covariance operators and Gaussian measures on Hilbert spaces ⋮ Quantum Jensen-Shannon divergences between infinite-dimensional positive definite operators

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