Flexibly regularized mixture models and application to image segmentation

DOI10.1016/j.neunet.2022.02.010arXiv1905.10629OpenAlexW3183059469MaRDI QIDQ6072505

Jonathan Vacher, Ruben Coen Cagli, Claire Launay

Publication date: 13 October 2023

Published in: Neural Networks (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1905.10629

zbMATH Keywords

unsupervised learningimage segmentationmixture modelsgraphical modelfactor graphconvolutional neural networks

Mathematics Subject Classification ID

Artificial intelligence (68Txx) Multivariate analysis (62Hxx)

Cites Work

• Natural image statistics. A probabilistic approach to early computational vision.
• On the convergence properties of the EM algorithm
• Model-based clustering of high-dimensional data: a review
• Factor graphs and the sum-product algorithm
• Multiple Kernel Sparse Representations for Supervised and Unsupervised Learning
• A Bayesian Framework for Image Segmentation With Spatially Varying Mixtures
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