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. 2021 Oct 28;12(5):20416695211054540.
doi: 10.1177/20416695211054540. eCollection 2021 Sep-Oct.

Textures vs Non-Textures: A Simple Computational Method for Classifying Perceived 'Texturality' in Natural Images

Fumiya Kurosawa  1 , Taiki Orima  2 , Kosuke Okada  2 , Isamu Motoyoshi  3
Affiliations

Textures vs Non-Textures: A Simple Computational Method for Classifying Perceived 'Texturality' in Natural Images

Fumiya Kurosawa et al. Iperception. .

Abstract

The visual system represents textural image regions as simple statistics that are useful for the rapid perception of scenes and surfaces. What images 'textures' are, however, has so far mostly been subjectively defined. The present study investigated the empirical conditions under which natural images are processed as texture. We first show that 'texturality' - i.e., whether or not an image is perceived as a texture - is strongly correlated with the perceived similarity between an original image and its Portilla-Simoncelli (PS) synthesized image. We found that both judgments are highly correlated with specific PS statistics of the image. We also demonstrate that a discriminant model based on a small set of image statistics could discriminate whether a given image was perceived as a texture with over 90% accuracy. The results provide a method to determine whether a given image region is represented statistically by the human visual system.

Keywords: natural image statistics; spatial vision; surfaces/materials; texture.

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Conflict of interest statement

Declaration of Conflicting Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Figures

Figure 1.
Figure 1.
Examples of natural images used in the experiment.
Figure 2.
Figure 2.
Relationship between the proportion at which the original image was classified as “texture” (horizontal axis) and the proportion at which the PS-synthesized image was perceived to be sufficiently similar to the original image (vertical axis). Each point represents the average of 8 observers.
Figure 3.
Figure 3.
Logistic correlations between PS statistics and judgments of texturality of the image (black) and similarity between synthetic and original image (grey). Each curve shows the results for different classes of summary image statistics plotted as a function of spatial frequency.
Figure 4.
Figure 4.
Examples of images classified as ‘texture’ (left) and ‘non-texture’ by the model with six summarized PS statistics.
See this image and copyright information in PMC

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