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Abstract

Distortions of image structure can go unnoticed in the visual periphery, and objects can be harder to identify (crowding). Is it possible to create equivalence classes of images that discard and distort image structure but appear the same as the original images? Here we use deep convolutional neural networks (CNNs) to study peripheral representations that are texture-like, in that summary statistics within some pooling region are preserved but local position is lost. Building on our previous work generating textures by matching CNN responses, we first show that while CNN textures are difficult to discriminate from many natural textures, they fail to match the
appearance of scenes at a range of eccentricities and sizes. Because texturising scenes discards long range correlations over too large an area, we next generate images that match CNN features within overlapping pooling regions (see also Freeman and Simoncelli, 2011). These images are more difficult to discriminate from the original scenes, indicating that constraining features by their
neighbouring pooling regions provides greater perceptual fidelity. Our ultimate goal is to determine the minimal set of deep CNN features that produce metameric stimuli by varying the feature complexity and pooling regions used to represent the image.