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Comparison of Outlier-Tolerant Models for Measuring Visual Complexity.

Adrian Carballal1,2, Carlos Fernandez-Lozano1,2, Nereida Rodriguez-Fernandez1,3

  • 1CITIC-Research Center of Information and Communication Technologies, University of A Coruña, 15071 A Coruña, Spain.

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Summary
This summary is machine-generated.

A novel machine learning approach, Correlation by Genetic Search (CGS), accurately predicts human perception of image visual complexity. This method outperforms existing models in correlation, RMSE, and feature efficiency, demonstrating robust variable selection.

Keywords:
compression errorcorrelationhuman-computer interactionmachine learningpsychiatry and psychologysisual complexityvisual stimuli

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Area of Science:

  • Computer Vision
  • Psychology
  • Machine Learning

Background:

  • Quantifying image visual complexity is crucial for applications in psychology and marketing.
  • Computer Vision research has explored computational models for image analysis.
  • Previous models have limitations in accurately predicting human aesthetic preferences.

Purpose of the Study:

  • To evaluate recent machine learning (ML) models for predicting human ratings of image visual complexity.
  • To compare the performance of different ML models based on human-evaluated image features.
  • To identify the most effective ML model for assessing visual complexity.

Main Methods:

  • Utilized recent ML models with human-evaluated image data.
  • Characterized images using features related to visual complexity.
  • Employed Correlation by Genetic Search (CGS) to find minimal feature sets maximizing model-data correlation.
  • Analyzed model performance using correlation, Root Mean Square Error (RMSE), and feature count.
  • Investigated model robustness by excluding outlier images.

Main Results:

  • Correlation by Genetic Search (CGS) demonstrated superior performance in predicting human ratings of image visual complexity.
  • CGS achieved higher correlation and lower RMSE compared to other referenced models.
  • CGS required a minimal set of features for accurate prediction.
  • The method showed robustness even when excluding outlier data points.

Conclusions:

  • Correlation by Genetic Search (CGS) is a highly effective ML model for predicting image visual complexity.
  • CGS offers a more accurate and efficient approach compared to existing methods.
  • The findings have significant implications for fields relying on visual perception analysis, such as marketing and psychology.