The role of texture summary statistics in material recognition from drawings and photographs

Material depictions in artwork are useful tools for revealing image features that support material categorization. For example, artistic recipes for drawing specific materials make explicit the critical information leading to recognizable material properties (Di Cicco, Wjintjes, & Pont, 2020) and investigating the recognizability of material renderings as a function of their visual features supports conclusions about the vocabulary of material perception. Here, we examined how the recognition of materials from photographs and drawings was affected by the application of the Portilla-Simoncelli texture synthesis model. This manipulation allowed us to examine how categorization may be affected differently across materials and image formats when only summary statistic information about appearance was retained. Further, we compared human performance to the categorization accuracy obtained from a pretrained deep convolutional neural network to determine if observers' performance was reflected in the network. Although we found some similarities between human and network performance for photographic images, the results obtained from drawings differed substantially. Our results demonstrate that texture statistics play a variable role in material categorization across rendering formats and material categories and that the human perception of material drawings is not effectively captured by deep convolutional neural networks trained for object recognition.