Category level object segmentation by combining bag-of-words models with Dirichlet processes and random fields

DOI10.1007/S11263-009-0245-XzbMath1477.68389OpenAlexW2123504458MaRDI QIDQ408785

Jakob Verbeek, Diane Larlus, Frédéric Jurie

Publication date: 12 April 2012

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

Full work available at URL: https://hal.inria.fr/inria-00439303/file/segmentation.pdf

zbMATH Keywords

random fieldssegmentationobject recognition

Mathematics Subject Classification ID

Random fields; image analysis (62M40) Computing methodologies for image processing (68U10) Machine vision and scene understanding (68T45)

Related Items (2)

Semantic Image Segmentation: Two Decades of Research ⋮ Error aware multiple vertical planes based visual localization for mobile robots in urban environments

Uses Software

• GrabCut
• SIFT
• PASCAL VOC

Cites Work

• Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
• 10.1162/jmlr.2003.3.4-5.993
• Extremely randomized trees
• Unsupervised learning by probabilistic latent semantic analysis
• Unnamed Item

This page was built for publication: Category level object segmentation by combining bag-of-words models with Dirichlet processes and random fields