ArticleBehavior research methods2025
A toolbox for calculating quantitative image properties in aesthetics research.
Article in Behavior research methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Preserved Aesthetic Judgements in Parkinson's Disease: A Case-Control Study Suggests Limited Need for Content Adaptation for Receptive Arts Engagement.Journal of clinical medicine · 2026Article
- Harmonious color pairings: Insights from human preference and natural hue statistics.iScience · 2026Article
- Latent neural architecture organising shared aesthetic evaluations of visual artworks.Nature communications · 2026Article
- Generative adversarial networks enable biomimetic topology fusion with balanced mechanical performance and aesthetic quality.Scientific reports · 2026Article
- The Minimum Semantic Content (MSC) Dataset: A Large, Balanced Resource for Computational Aesthetics Research.Scientific data · 2026Article
- Partisan aesthetics: political orientation modulates aesthetic judgments of propaganda posters.Frontiers in psychology · 2026Article
- The temporal and spatial properties of memorability reveal insights into the art creation process.Cognition · 2026Article
- Preference for symmetry, balance, or proximity in picture aesthetics depends on the method of evaluation.i-PerceptionArticle
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Authors and funding
7 authors.
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Abstract
Over the past two decades, researchers in the field of visual aesthetics have studied numerous quantitative (objective) image properties and how they relate to visual aesthetic appreciation. However, results are difficult to compare between research groups. One reason is that researchers use different sets of image properties in their studies. However, even if the same properties are used, the image pre-processing techniques may differ, and researchers often use their own customized scripts to calculate the image properties. To provide better accessibility and comparability of research results in visual experimental aesthetics, we developed an open-access and easy-to-use toolbox called Aesthetics Toolbox. The Toolbox allows users to calculate a well-defined set of quantitative image properties popular in contemporary research. The properties include image dimensions, lightness and color statistics, complexity, symmetry, balance, Fourier spectrum properties, fractal dimension, self-similarity, as well as entropy measures and CNN-based variances. Compatible with most devices, the Toolbox provides an intuitive click-and-drop web interface. In the Toolbox, we integrated the original scripts of four different research groups and translated them into Python 3. To ensure that results were consistent across analyses, we took care that results from the Python versions of the scripts were the same as those from the original scripts. The toolbox, detailed documentation, and a link to the cloud version are available via GitHub: https://github.com/RBartho/Aesthetics-Toolbox . In summary, we developed a toolbox that helps to standardize and simplify the calculation of quantitative image properties for visual aesthetics research.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.