PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Brain Responses Strongly Correlate with Weibull Image Statistics when Processing Natural Images
H. S. Scholte, Sennay Ghebreab, L. Waldorp, Arnold W.M. Smeulders and V.A.F. Lamme
Journal of Vision Volume 9, Number 4, pp. 1-15, 2009.

Abstract

The visual appearance of natural scenes is governed by a surprisingly simple hidden structure. The distributions of contrast values in natural images generally follow a Weibull distribution, with beta and gamma as free parameters. Beta and gamma seem to structure the space of natural images in an ecologically meaningful way, in particular with respect to the fragmentation and texture similarity within an image. Since it is often assumed that the brain exploits structural regularities in natural image statistics to efficiently encode and analyze visual input, we here ask ourselves whether the brain approximates the beta and gamma values underlying the contrast distributions of natural images. We present a model that shows that beta and gamma can be easily estimated from the outputs of X-cells and Y-cells. In addition, we covaried the EEG responses of subjects viewing natural images with the beta and gamma values of those images. We show that beta and gamma explain up to 71% of the variance of the early ERP signal, substantially outperforming other tested contrast measurements. This suggests that the brain is strongly tuned to the image's beta and gamma values, potentially providing the visual system with an efficient way to rapidly classify incoming images on the basis of omnipresent low-level natural image statistics.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
Brain Computer Interfaces
ID Code:6158
Deposited By:Christof Monz
Deposited On:08 March 2010