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Related Experiment Video

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Creating Objects and Object Categories for Studying Perception and Perceptual Learning
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Predicting Complexity Perception of Real World Images.

Silvia Elena Corchs1,2, Gianluigi Ciocca1,2, Emanuela Bricolo3,2

  • 1Department of Informatics, Systems and Communication, University of Milano-Bicocca, Viale Sarca 336, 20126 Milano, Italy.

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Summary

This study introduces a novel image complexity measure by combining visual features. Optimized using Particle Swarm Optimization, it accurately predicts human perception of image complexity.

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Area of Science:

  • Computer Vision
  • Human-Computer Interaction
  • Perceptual Science

Background:

  • Image complexity is crucial for various applications, including image retrieval and user experience.
  • Existing measures often fail to capture the nuances of human perception of visual complexity.

Purpose of the Study:

  • To develop and validate a new computational model for predicting perceived image complexity.
  • To create a linear combination of image features that best correlates with subjective complexity ratings.

Main Methods:

  • A novel complexity measure was formulated by linearly combining spatial, frequency, and color image features.
  • Particle Swarm Optimization (PSO) was employed to determine optimal weighting coefficients for the feature combination.
  • Subjective data was collected through web-based experiments where participants rated image complexity.

Main Results:

  • The proposed complexity measure significantly outperformed individual visual features and existing visual clutter metrics.
  • The model demonstrated strong correlation with subjective complexity data in independent validation experiments.
  • The measure showed effectiveness across diverse image sets, including natural scenes and textures.

Conclusions:

  • The developed linear combination of visual features provides a robust predictor of perceived image complexity.
  • The approach is adaptable and can be tuned for different types of visual stimuli.
  • This work offers a valuable tool for applications requiring accurate prediction of image complexity.