Densifying distance spaces for shape and image retrieval

DOI10.1007/s10851-012-0363-xzbMath1312.68205OpenAlexW2032698077MaRDI QIDQ2513322

Xingwei Yang, Longin Jan Latecki, Xiang Bai, Suzan Köknar-Tezel

Publication date: 28 January 2015

Published in: Journal of Mathematical Imaging and Vision (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10851-012-0363-x

zbMATH Keywords

diffusion processghost pointsshape retrieval

Mathematics Subject Classification ID

Computing methodologies for image processing (68U10) Information storage and retrieval of data (68P20) Machine vision and scene understanding (68T45)

Uses Software

• SMOTEBoost
• SMOTE

Cites Work

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• Nonlinear dimensionality reduction by topologically constrained isometric embedding
• An efficient algorithm for the computation of the metric average of two intersecting convex polygons, with application to morphing
• Embedding Riemannian manifolds by their heat kernel
• Co-Transduction for Shape Retrieval
• Machine Learning: ECML 2004
• Hessian eigenmaps: Locally linear embedding techniques for high-dimensional data
• Computer Vision - ECCV 2004