Evaluating the WikiArt Dataset using Quantitative Image Properties, Ratings, and Art History
Date:
The WikiArt dataset is popular in research on art and aesthetics. However, closer inspection reveals occasional subpar image quality, possibly distorting the results of previous research. Here, we present a novel dataset on image quality and findings from a preregistered online study to investigate aesthetic appreciation in relation to common image distortions in WikiArt. Our stimulus set (750 images, 25 art styles) was curated by an art historian, who identified four quality issues within the WikiArt dataset. Many images are either blurred, cropped, enhanced, outdated or a combination of these factors. All 250 (50 per issue) low-quality images from WikiArt were labeled and matched with two high-quality counterparts. We used 38 quantitative image properties (QIPs) to specify the four quality issues labelled by the expert. A one-way MANOVA revealed a strong multivariate effect (V = 0.935, p<.001) of label membership on the QIPs. To reduce dimensionality, we performed PCA and FA and found that the first 5 to 7 components captured ~58% to 60% of total variance, respectively. To investigate the effect of image quality (low vs. high) on aesthetic appreciation, each participant in an online study (N=332) rated 100 images with a Slider Rating Scale (0-100). We also collected demographic information, familiarity with the artworks as well as art expertise. A Mixed Effects Bayesian Regression indicates image quality had only a small, nonsignificant negative effect on the ratings (Est. -1.33, 95%CI[-3.17, 0.53]), even including expertise (Est.-4.13, 95%CI[-10.90, 2.53]) and its interaction with quality (Est. 1.04, 95%CI[-0.92, 2.95]). We provide a theoretical and quantitatively validated dataset of quality distortions in artworks in WikiArt. Model results suggest the quality issues do not influence aesthetic appreciation in a one- image-at-a-time rating task. For deeper insights a follow-up study of direct image comparisons across quality versions is currently being conducted. Acknowledgment: This work is funded by an ERC Advanced Grant (No. 101053925, GRAPPA) awarded to JW.
Recommended citation: Koßmann, L., De Winter, S., Woussen, J., Bossens, C. & Wagemans, J. (2025). Evaluating the WikiArt Dataset using Quantitative Image Properties, Ratings, and Art History [Poster]. 47th European Conference on Visual Perception (ECVP), Mainz, Germany.