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A deep-learning framework for human perception of abstract art composition.

Pierre Lelièvre1,2,3, Peter Neri2,4,5

  • 1Laboratoire des systèmes perceptifs, Département d'études cognitives Science Arts Création Recherche (EA 7410), Paris, France.

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Summary
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This study introduces a deep learning model to quantitatively analyze artistic composition, successfully predicting human orientation judgments across diverse painting styles. The model offers new insights into visual perception and art analysis.

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Area of Science:

  • Computational Vision
  • Art History
  • Cognitive Science

Background:

  • Artistic composition lacks quantitative analysis tools in art history.
  • Existing models for orientation judgments rely on specific content or rules.
  • Human ability to determine painting orientation, even abstract art, is not fully explained.

Purpose of the Study:

  • To develop a quantitative metric for artistic composition using deep learning.
  • To investigate the perceptual mechanisms underlying human orientation judgments in art.
  • To outperform existing models in analyzing a wide range of painting styles.

Main Methods:

  • Utilized a deep-learning algorithm to model human perceptual mechanisms for composition.
  • Employed orientation judgments (determining if a painting is right-side up) as a behavioral marker.
  • Validated the model against human performance in web-based experiments with full paintings and fragments.

Main Results:

  • The deep learning model significantly outperforms previous models on a large, diverse painting database.
  • The model accurately reproduces human performance in orientation judgment tasks.
  • Abstract paintings prompted the model to rely more on extended spatial integration, supported by deeper layers.

Conclusions:

  • Deep learning can provide quantitative insights into artistic composition and human visual perception.
  • The model captures key characteristics of human orientation perception across different styles and granularities.
  • The findings suggest a link between abstractness, spatial integration, and deep visual processing in art perception.