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Predicting human complexity perception of real-world scenes.

Fintan Nagle1, Nilli Lavie1

  • 1Institute of Cognitive Neuroscience, University College London, London, UK.

Royal Society Open Science
|June 16, 2020
PubMed
Summary
This summary is machine-generated.

Researchers developed a deep convolutional neural network (CNN) to measure visual complexity, offering a new way to quantify perceptual load in real-world images. This model accurately predicts perceived visual complexity, advancing attentional engagement studies.

Keywords:
attentionperceptual loadvisual complexity

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Area of Science:

  • Cognitive Psychology
  • Computer Vision
  • Neuroscience

Background:

  • Perceptual load influences attentional engagement, traditionally studied with simple visual stimuli.
  • Existing methods for manipulating perceptual load and measuring visual complexity are limited for real-world images.

Purpose of the Study:

  • To develop a robust model for quantifying visual complexity in diverse, real-world images.
  • To establish visual complexity as a proxy for perceptual load in complex visual scenes.

Main Methods:

  • Trained a deep convolutional neural network (CNN) on perceived visual complexity ratings from 53 observers viewing 4000 PASCAL VOC images.
  • Collected 75,020 paired comparisons to derive image visual complexity scores using the TrueSkill algorithm.
  • Compared CNN performance against traditional feature-based image statistics models.

Main Results:

  • The CNN model achieved a high correlation (r = 0.83) in predicting perceived visual complexity.
  • Traditional models based on image statistics (entropy, edge density, JPEG ratio) showed lower predictive power (r = 0.70).

Conclusions:

  • A CNN-based approach effectively models visual complexity for real-world images.
  • This method provides a reliable tool for operationalizing perceptual load in complex visual environments, advancing research in attention and perception.