A toolbox for calculating objective image properties in aesthetics research
<|reference_start|>A toolbox for calculating objective image properties in aesthetics research: 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. But even if the same properties are used, the image pre-processing techniques may differ and often researchers use their own customized scripts to calculate the image properties. To provide greater accessibility and comparability of research results in visual experimental aesthetics, we developed an open-access and easy-to-use toolbox (called the 'Aesthetics Toolbox'). The Toolbox allows users to calculate a well-defined set of quantitative image properties popular in contemporary research. The properties include lightness and color statistics, Fourier spectral properties, fractality, self-similarity, symmetry, as well as different 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.<|reference_end|>
@article{redies2024a,
    title={A toolbox for calculating objective image properties in aesthetics
  research},
    author={Christoph Redies, Ralf Bartho, Lisa Ko{ss}mann, Branka Spehar, Ronald
  H"ubner, Johan Wagemans, Gregor U. Hayn-Leichsenring},
    journal={arXiv preprint arXiv:2408.10616},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.10616},
    primaryClass={cs.CV stat.AP}
}