Symmetry Detection in Images of Natural Scenes by Humans and Machines
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Although mirror symmetry is an established and popular principle of perceptual organization, human symmetry detection in images of natural scenes remains highly understudied, when compared to symmetry detection in artificially created dot patterns and shapes. In this multidisciplinary project, we investigate human symmetry detection in 100 images of natural scenes in relation to quantitative metrics derived from computer vision and machine learning. In our study participants were asked to place a rectangular bounding box around an image region they perceived as mirror-symmetric and to indicate the axis of symmetry. They could place as many bounding boxes as they saw fit. For each of them, they also rated the perceived saliency of the region (i.e., how much it popped-out from the background) and the strength of the symmetry (i.e., from rather imperfect to almost perfect symmetry). Statistical analysis of 2173 symmetries by 17 participants so far reveals that participants selected bigger, more salient regions of symmetry first. Vertical axes were much more frequent (around 75%) than horizontal and oblique ones. Horizontally and vertically symmetric regions were found to be more salient and more symmetric than oblique ones. Saliency and strength ratings were moderately correlated (around 0.4) across all regions and images. We used different metrics for image quality assessment to compute symmetry accuracy scores for the bounding boxes, revealing large discrepancies between human and computational symmetry assessment (correlations below 0.1), both for saliency and strength. This emphasizes the need to go beyond traditional computer vision algorithms and employ deep learning models. Human data collection is still ongoing, and we also plan to train a deep learning model on symmetry detection and present it alongside these findings. Open data and methods: https://osf.io/9tf4e/ Acknowledgment: This work is funded by an ERC Advanced Grant (No. 101053925) awarded to JW.
Recommended citation: Koßmann, L., Muradás Odriozola, G., Bossens, C. & Wagemans, J. (2023). Symmetry Detection in Images of Natural Scenes by Humans and Machines [Poster]. 45th European Conference on Visual Perception (ECVP), Paphos, Cyprus.