Learning Invariant Features Using Inertial Priors
Venue
Annals of Mathematics and Artificial Intelligence, vol. 47 (2006), pp. 223-250
Publication Year
2006
Authors
BibTeX
Abstract
We address the technical challenges involved in combining key features from several
theories of the visual cortex in a single coherent model. The resulting model is a
hierarchical Bayesian network factored into modular component networks embedding
variable-order Markov models. Each component network has an associated receptive
field corresponding to components residing in the level directly below it in the
hierarchy. The variable-order Markov models account for features that are invariant
to naturally occurring transformations in their inputs. These invariant features
give rise to increasingly stable, persistent representations as we ascend the
hierarchy. The receptive fields of proximate components on the same level overlap
to restore selectivity that might otherwise be lost to invariance.
