Non-linear Bayesian Image Modelling

417 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Variational Inference: A Review for Statisticians (2016, background): “Some of the earliest examples included inferring nonlinear image manifolds (Bishop and Winn 2000) and finding layers of images in videos (Jojic and Frey 2001).”
Pattern Recognition and Machine Learning (2006, neutral): “A fully Bayesian treatment, based on variational inference (Bishop and Winn, 2000), allows the number of components in the mixture, as well as the effective dimensionalities of the individual models, to be inferred from the data.”
Variational Bayesian Model Selection for Mixture Distributions (2001, methodology): “The problem has also been approached from a Bayesian perspective using reversible jump Markov chain Monte Carlo [7] and using variational methods [1, 6, 4].”
Video-based face recognition using probabilistic appearance manifolds (2003, background): “We conclude with remarks and future work in Section 5.”
Variational Message Passing (2005, methodology): “As a demonstration of its utility, this algorithm has already been used to solve problems in the domain of machine vision and bioinformatics (see Winn, 2003; Bishop and Winn, 2000).”