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Gestalt principles provide a framework for understanding how humans perceive objects as unified wholes within their context. These principles are essential in explaining the cognitive processes that make sense of complex visual stimuli by organizing them into coherent groups. One fundamental principle is proximity, which posits that objects located close to each other are perceived as a collective group. For instance, when dots are positioned near one another, the visual system interprets them...
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Characterising and dissecting human perception of scene complexity.

Cameron Kyle-Davidson1, Elizabeth Yue Zhou2, Dirk B Walther3

  • 1University of York, Department of Computer Science, York, YO10 5GH, UK.

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Humans perceive visual complexity using both low-level image properties and high-level semantic information. This study introduces new datasets and a "dual information" framework to explain this complex visual perception.

Keywords:
Human visionModelling visionNeural networksPerceptual complexityScene complexity

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Area of Science:

  • Cognitive Psychology
  • Computer Vision
  • Neuroscience

Background:

  • Human ability to assess visual complexity is effortless but poorly understood.
  • Natural scenes present a constant challenge for complexity perception research.
  • Existing models often overlook the interplay between low-level features and semantics.

Purpose of the Study:

  • To investigate the factors influencing human perception of scene complexity.
  • To develop and validate computational models for predicting perceived complexity.
  • To propose a novel framework explaining dual information processing in visual complexity.

Main Methods:

  • Creation of two novel datasets (VISC-C and VISC-CI) with human complexity annotations.
  • Analysis of perceptual features (clutter, symmetry, entropy, openness) using hierarchical regression.
  • Evaluation of neural network models and validation against a large complexity dataset.
  • Dissection of best-performing neural networks to understand feature extraction.

Main Results:

  • Both global image properties and semantic features significantly contribute to complexity perception.
  • Combining perceptual features with semantic network output improved variance explanation.
  • Neural networks learn to extract both low-level and high-level scene details for complexity prediction.

Conclusions:

  • Human complexity perception relies on a "dual information" framework, integrating low-level and high-level processing.
  • Novel datasets and analyses provide a foundation for future research in visual complexity.
  • Computational models can effectively predict human complexity perception by incorporating semantic understanding.