Unsupervised learning for graph matching

DOI10.1007/s11263-011-0442-2zbMath1235.68274OpenAlexW2152953631MaRDI QIDQ409138

Martial Hebert, Rahul Sukthankar, Marius Leordeanu

Publication date: 12 April 2012

Published in: International Journal of Computer Vision (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11263-011-0442-2

zbMATH Keywords

unsupervised learninggraph matchingfeature matchingparameter learningsemi-supervised learningMAP inference

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Graph theory (including graph drawing) in computer science (68R10) Machine vision and scene understanding (68T45)

Related Items

Unsupervised learning for graph matching ⋮ Efficient feature matching via nonnegative orthogonal relaxation ⋮ Graph matching by simplified convex-concave relaxation procedure ⋮ Seeking affinity structure: strategies for improving \(m\)-best graph matching ⋮ Diffusion pruning for rapidly and robustly selecting global correspondences using local isometry ⋮ ROML: a robust feature correspondence approach for matching objects in a set of images ⋮ Vertex nomination via seeded graph matching ⋮ Image matching from handcrafted to deep features: a survey ⋮ Distance metric learning for graph structured data

Cites Work

• Unsupervised learning for graph matching
• An eigendecomposition approach to weighted graph matching problems
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