Refereed full papers (journals, book chapters, international conferences)

2000

@inproceedings{BiW2000,
	vgclass =	{refpap},
	author =	{Christopher M. Bishop and  John M. Winn},
	title =	{Non-linear {B}ayesian image modelling},
	booktitle =	{Proceedings of the Sixth European Conference on Computer
	Vision},
	address =	{Dublin},
	pages =	{3--17},
	year =	{2000},
	url =	{http://research.microsoft.com/~cmbishop/downloads/Bishop-ECCV00.ps},
	abstract =	{In recent years several techniques have been proposed for
	modelling the low-dimensional manifolds, or `subspaces', of natural
	images. Examples include principal component analysis (as used for
	instance in `eigen-faces'), independent component analysis, and
	auto-encoder neural networks. Such methods suffer from a number of
	restrictions such as the limitation to linear manifolds or the absence
	of a probabilistic representation. In this paper we exploit recent
	developments in the fields of variational inference and latent variable
	models to develop a novel and tractable probabilistic approach to
	modelling manifolds which can handle complex non-linearities. Our
	framework comprises a mixture of sub-space components in which both the
	number of components and the effective dimensionality of the sub-spaces
	are determined automatically as part of the Bayesian inference
	procedure. We illustrate our approach using two classical problems:
	modelling the manifold of face images and modelling the manifolds of
	hand-written digits.},
}