Towards matching peripheral appearance for arbitrary natural images using deep features

Abstract

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 (i.e. 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 can discriminate model-generated images from their original source images. Three images subtending 25 deg, two of which were physically identical, were presented for 200 ms each in a three-alternative temporal oddity paradigm. We find a model that, for most original images we tested, produces synthesised images that cannot be told apart from the originals despite producing significant distortions of image structure. However, some images were readily discriminable. Therefore, the model has successfully encoded necessary but not sufficient information to capture appearance in human scene perception. We explore what image features are correlated with discriminability on the image (which images are harder than others?) and pixel (where in an image is the hardest location?) level. While our model does not produce "metamers", it does capture many features important for the appearance of arbitrary natural images in the periphery.