Learning linear assignment flows for image labeling via exponential integration

DOI10.1007/978-3-030-75549-2_31zbMath1484.68310OpenAlexW3159709631MaRDI QIDQ826205

Christoph Schnörr, Alexander Zeilmann, Stefania Petra

Publication date: 20 December 2021

Full work available at URL: https://doi.org/10.1007/978-3-030-75549-2_31

zbMATH Keywords

low-rank approximationparameter learningimage labelingassignment manifoldexponential integrationlinear assignment flows

Mathematics Subject Classification ID

Computing methodologies for image processing (68U10) Numerical methods for initial value problems involving ordinary differential equations (65L05) Numerical computation of matrix exponential and similar matrix functions (65F60)

Related Items (2)

Learning linearized assignment flows for image labeling ⋮ Assignment flows for data labeling on graphs: convergence and stability

Cites Work

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• Approximation of functions of large matrices with Kronecker structure
• The ubiquitous Kronecker product
• Image labeling by assignment
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• Algorithm 919
• Assignment Flows
• Computing the Fréchet Derivative of the Matrix Exponential, with an Application to Condition Number Estimation
• On Krylov Subspace Approximations to the Matrix Exponential Operator
• Nineteen Dubious Ways to Compute the Exponential of a Matrix, Twenty-Five Years Later
• Geometric numerical integration of the assignment flow
• Functions of Matrices

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