December 2022
Volume 22, Issue 14
Open Access
Vision Sciences Society Annual Meeting Abstract  |   December 2022
The role of texture summary-statistics in material recognition from drawings and photographs
Author Affiliations
  • Benjamin Balas
    North Dakota State University
  • Michelle Greene
    Bates College
Journal of Vision December 2022, Vol.22, 3583. doi:
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      Benjamin Balas, Michelle Greene; The role of texture summary-statistics in material recognition from drawings and photographs. Journal of Vision 2022;22(14):3583.

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      © ARVO (1962-2015); The Authors (2016-present)

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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 et al., 2020) and investigating the recognizability of material renderings as a function of their visual features supports conclusions about the vocabulary of material perception. Presently, we compared material categorization abilities between photographic stimuli (Sharan et al., 2014) and line drawings (Saito et al., 2015) in their original format and after texture synthesis. Specifically, our participants (N=52) completed a 4AFC material categorization task in which stimulus appearance was manipulated across participant groups via 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. Our accuracy results revealed a three-way interaction (F(3,150)=4.80, p=0.003) between image format (photographs/drawings), material category (metal, stone, water, or wood) and appearance (original/texture-synthesized, driven by differential advantages for photographic vs. line drawing stimuli as a function of both material category and appearance. While line drawings supported better recognition of metal and wood across all stimulus manipulations, photographic water and stone were better recognized than drawings in original and synthetic versions, respectively. Do these patterns emerge purely from the image data? A linear SVM classifier assessed discriminability of the four materials in the eight layers of a deep convolutional neural network (AlexNet). Classification accuracy increased across layers, and photographs out-performed drawings. Category confusion rates were more correlated across humans and the classifier for photographs as well. Together, these results demonstrate that line drawings can make materials more recognizable to humans than photographs in some cases, perhaps by isolating critical image features used by human vision that are not captured by pre-trained dCNNs.


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