Towards matching peripheral appearance for arbitrary natural images using deep features


Due to the structure of the primate visual system, large distortions of the input can go unnoticed in the periphery, and objects can be harder to identify. What encoding underlies these effects? Similarly to Freeman & Simoncelli (Nature Neuroscience, 2011), we developed a model that uses summary statistics averaged over spatial regions that increases with retinal eccentricity (assuming central fixation on an image). We also designed the averaging areas such that changing their scaling progressively discards more information from the original image (ie a coarser model produces greater distortions to original image structure than a model with higher resolution). Different from Freeman and Simoncelli, we use the features of a deep neural network trained on object recognition (the VGG-19; Simonyan & Zisserman, ICLR 2015), which is state-of-the art in parametric texture synthesis. We tested whether human observers …

Matthias Bethge
Matthias Bethge
Professor for Computational Neuroscience and Machine Learning & Director of the Tübingen AI Center

Matthias Bethge is Professor for Computational Neuroscience and Machine Learning at the University of Tübingen and director of the Tübingen AI Center, a joint center between Tübingen University and MPI for Intelligent Systems that is part of the German AI strategy.