Attentive texture similarity as a categorization task: Comparing texture synthesis models

Abstract

Many attempts have been made to characterize latent structures in “texture spaces” defined by attentive similarity judgments. While an optimal description of perceptual texture space remains elusive, we suggest that the similarity judgments gained from these procedures provide a useful standard for relating image statistics to high-level similarity. In the present experiment, we ask subjects to group natural textures into visually similar clusters. We also represent each image using the features employed by three different parametric texture synthesis models. Given the cluster labels for our textures, we use linear discriminant analysis to predict cluster membership. We compare each model's assignments to human data for both positive and contrast-negated textures, and evaluate relative model performance.