Sciweavers

Share
IJCNN
2008
IEEE

On the learning of nonlinear visual features from natural images by optimizing response energies

8 years 10 months ago
On the learning of nonlinear visual features from natural images by optimizing response energies
— The operation of V1 simple cells in primates has been traditionally modelled with linear models resembling Gabor filters, whereas the functionality of subsequent visual cortical areas is less well understood. Here we explore the learning of mechanisms for further nonlinear processing by assuming a functional form of a product of two linear filter responses, and estimating a basis for the given visual data by optimizing for robust alternative of variance of the nonlinear model outputs. By a simple transformation of the learned model, we demonstrate that on natural images, both minimization and maximization in our setting lead to oriented, band-pass and localized linear filters whose responses are then nonlinearly combined. In minimization, the method learns to multiply the responses of two Gabor-like filters, whereas in maximization it learns to subtract the response magnitudes of two Gabor-like filters. Empirically, these learned nonlinear filters appear to function as conjun...
Jussi T. Lindgren, Aapo Hyvärinen
Added 31 May 2010
Updated 31 May 2010
Type Conference
Year 2008
Where IJCNN
Authors Jussi T. Lindgren, Aapo Hyvärinen
Comments (0)
books