A. Hyvarinen et P. Hoyer, Emergence of phase- and shift-invariant features by decomposition of natural images into independent feature subspaces, NEURAL COMP, 12(7), 2000, pp. 1705-1720
Citations number
24
Categorie Soggetti
Neurosciences & Behavoir","AI Robotics and Automatic Control
Olshausen and Field (1996) applied the principle of independence maximizati
on by sparse coding to extract features from natural images. This leads to
the emergence of oriented linear filters that have simultaneous localizatio
n in space and in frequency, thus resembling Gabor functions and simple cel
l receptive fields. In this article, we show that the same principle of ind
ependence maximization can explain the emergence of phase- and shift-invari
ant features similar to those found in complex cells. This new kind of emer
gence is obtained by maximizing the independence between norms of projectio
ns on linear subspaces (instead of the independence of simple linear filter
outputs). The norms of the projections on such "independent feature subspa
ces" then indicate the values of invariant features.