ArticleFrontiers in psychology2017
Using CNN Features to Better Understand What Makes Visual Artworks Special.
Article in Frontiers in psychology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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18 citing papers in PubMed.
- A toolbox for calculating quantitative image properties in aesthetics research.Behavior research methods · 2025Article
- The perceptual primacy of feeling: Affectless visual machines explain a majority of variance in human visually evoked affect.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Direct perception of affective valence from vision.Nature communications · 2024Article
- Emergence of Emotion Selectivity in Deep Neural Networks Trained to Recognize Visual Objects.bioRxiv : the preprint server for biology · 2024Article
- Emergence of Emotion Selectivity in Deep Neural Networks Trained to Recognize Visual Objects.PLoS computational biology · 2024Article
- Sparsity in an artificial neural network predicts beauty: Towards a model of processing-based aesthetics.PLoS computational biology · 2023Article
- Statistical image properties and aesthetic judgments on abstract paintings by Robert Pepperell.Journal of vision · 2023Article
- From signals to music: a bottom-up approach to the structure of neuronal activity.Frontiers in systems neuroscience · 2023Article
- Research on Emotion Analysis and Psychoanalysis Application With Convolutional Neural Network and Bidirectional Long Short-Term Memory.Frontiers in psychology · 2022Article
- Statistical image properties predict aesthetic ratings in abstract paintings created by neural style transfer.Frontiers in neuroscience · 2022Article
- Orbital and eyelid diseases: The next breakthrough in artificial intelligence?Frontiers in cell and developmental biology · 2022Review
- Fractality and Variability in Canonical and Non-Canonical English Fiction and in Non-Fictional Texts.Frontiers in psychology · 2021Article
- Global Image Properties Predict Ratings of Affective Pictures.Frontiers in psychology · 2020Article
- Statistical Image Properties in Large Subsets of Traditional Art,Frontiers in neuroscience · 2017Article
- Computational and Experimental Approaches to Visual Aesthetics.Frontiers in computational neuroscience · 2017Review
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- Aesthetic Perception of Line Patterns: Effect of Edge-Orientation Entropy and Curvilinear Shape.i-PerceptionArticle
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3 authors.
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Abstract
One of the goal of computational aesthetics is to understand what is special about visual artworks. By analyzing image statistics, contemporary methods in computer vision enable researchers to identify properties that distinguish artworks from other (non-art) types of images. Such knowledge will eventually allow inferences with regard to the possible neural mechanisms that underlie aesthetic perception in the human visual system. In the present study, we define measures that capture variances of features of a well-established Convolutional Neural Network (CNN), which was trained on millions of images to recognize objects. Using an image dataset that represents traditional Western, Islamic and Chinese art, as well as various types of non-art images, we show that we need only two variance measures to distinguish between the artworks and non-art images with a high classification accuracy of 93.0%. Results for the first variance measure imply that, in the artworks, the subregions of an image tend to be filled with pictorial elements, to which many diverse CNN features respond (
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