Source (or Part of the following Source): Type Article Title Brain Responses Strongly Correlate with Weibull Image Statistics When Processing Natural Images Author(s) Introduction

Copyright It is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content licence (like Creative Commons). UvA-DARE is a service provided by the library of the University of Amsterdam (http://dare.uva.nl) Brain responses strongly correlate with Weibull image statistics when processing natural images 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. strongly correlate with Weibull image statistics when processing natural images.

[1]  G. Rousselet,et al.  Parametric study of EEG sensitivity to phase noise during face processing , 2008, BMC Neuroscience.

[2]  W. Geisler.  Visual perception and the statistical properties of natural scenes. , 2008, Annual review of psychology.

[3]  E. Adelson,et al.  Image statistics and the perception of surface qualities , 2007, Nature.

[4]  Victor A. F. Lamme,et al.  The influence of inattention on the neural correlates of scene segmentation , 2006, Brain Research.

[5]  R. Millane,et al.  Effects of occlusion, edges, and scaling on the power spectra of natural images. , 2005, Journal of the Optical Society of America. A, Optics, image science, and vision.

[6]  Heinrich H. Bülthoff,et al.  Low-Level Image Cues in the Perception of Translucent Materials , 2005, TAP.

[7]  Arnold W. M. Smeulders,et al.  c ○ 2005 Springer Science + Business Media, Inc. Manufactured in The Netherlands. A Six-Stimulus Theory for Stochastic Texture , 2002 .

[8]  Mary M. Conte,et al.  Interaction of luminance and higher-order statistics in texture discrimination , 2005, Vision Research.

[9]  Paul Over,et al.  Multimedia retrieval benchmarks , 2004, IEEE MultiMedia.

[10]  S. Thorpe,et al.  A Limit to the Speed of Processing in Ultra-Rapid Visual Categorization of Novel Natural Scenes , 2001, Journal of Cognitive Neuroscience.

[11]  M. Thomson.  Beats, kurtosis and visual coding , 2001, Network.

[12]  Eero P. Simoncelli.  Modeling the joint statistics of images in the wavelet domain , 1999, Optics & Photonics.

[13]  J. Bert Keats,et al.  Statistical Methods for Reliability Data , 1999 .

[14]  J. Nadal,et al.  Self-Similarity Properties of Natural Images Resemble Those of Turbulent Flows , 1998, cond-mat/0107314.

[15]  A. Oliva,et al.  Coarse Blobs or Fine Edges? Evidence That Information Diagnosticity Changes the Perception of Complex Visual Stimuli , 1997, Cognitive Psychology.

[16]  William Bialek,et al.  Statistics of Natural Images: Scaling in the Woods , 1993, NIPS.

[17]  Petre Stoica,et al.  MUSIC, maximum likelihood, and Cramer-Rao bound , 1989, IEEE Transactions on Acoustics, Speech, and Signal Processing.

[18]  D J Field,et al.  Relations between the statistics of natural images and the response properties of cortical cells. , 1987, Journal of the Optical Society of America. A, Optics and image science.

[19]  C. Enroth-Cugell,et al.  The receptive‐field spatial structure of cat retinal Y cells. , 1987, The Journal of physiology.

[20]  Benoit B. Mandelbrot,et al.  Fractal Geometry of Nature , 1984 .

[21]  K. Chung,et al.  Limit Distributions for Sums of Independent Random Variables , 1955 .

[22]  J. Koenderink.  The structure of images , 2004, Biological Cybernetics.

[23]  A. Smeulders,et al.  A Physical Explanation for Natural Image Statistics , 2002 .

[24]  D. Tolhurst,et al.  The human visual system is optimised for processing the spatial information in natural visual images , 2000, Current Biology.

[25]  L. Croner,et al.  Receptive fields of P and M ganglion cells across the primate retina , 1995, Vision Research.

[26]  D. Hubel,et al.  Receptive fields, binocular interaction and functional architecture in the cat's visual cortex , 1962, The Journal of physiology.

[27]  E. Brunswik,et al.  Ecological cue-validity of proximity and of other Gestalt factors. , 1953, The American journal of psychology.