Using patch-based image synthesis to measure perceptual texture similarity

Abstract

The perceptual similarity of textures has gained considerable attention in the computer vision and graphics communities. Here, we focus on the challenging task of estimating the mutual perceptual similarity between two textures from materials on a consistent scale. Unlike previous studies that more or less directly queried pairwise similarity from human subjects, we propose an indirect approach that is inspired by the notion of just-noticeable differences (JND). Similar metrics are common in imaging and color science, but so far have not been directly transferred to textures, since they require the generation of intermediate stimuli. Using patch-based statistical texture synthesis, we produce continuous transitions between pairs of textures. In a user experiment, participants are then asked to locate an interpolated specimen in the linear continuum. Our intuition is that the JND, defined as the uncertainty with which participants perform this task, is closely related with the perceived pairwise texture similarity. Using a dataset of fabric textures, we show that this metric is particularly suitable to address fine-grained similarities, produces approximately interval-scale measurements and is additionally convenient for crowdsourcing. We further validate our approach by comparing it with a well-established data collection technique using the same dataset.