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Shaping the aesthetical landscape by using image statistics measures.

F Bertacchini1, P S Pantano2, E Bilotta2

  • 1Department of Mechanical, Energy and Management Engineering, University of Calabria, Cubo 43, 5th floor, Ponte P. Bucci, 87036 Arcavacata di Rende, CS, Italy; Laboratory of Cognitive Science and Mathematical Modelling, Department of Physics, University of Calabria, Cubo 17b, 6th floor, Ponte P. Bucci, 87036 Arcavacata di Rende, CS, Italy.

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Summary
This summary is machine-generated.

This study introduces machine learning and mathematical methods to automatically evaluate object aesthetics. It quantifies aesthetic features in art images, mapping them within a defined aesthetic space.

Keywords:
Aesthetic Parametric SpaceMachine Learning AlgorithmsMultidimensional Space Visualization

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Area of Science:

  • Computer Science
  • Art Analysis
  • Data Science

Background:

  • Assessing object aesthetics is crucial in modern society.
  • Existing computational aesthetics lack formalization and visualization.
  • Human intervention is a limitation in current aesthetic evaluation.

Purpose of the Study:

  • To develop automatic methods for aesthetic evaluation.
  • To formalize and visualize the aesthetic field.
  • To extract key features for computational aesthetic analysis.

Main Methods:

  • Utilized Machine Learning techniques for feature extraction.
  • Applied mathematical methods for quantitative analysis.
  • Empirically tested on 83 artist-produced images of triangles.

Main Results:

  • Successfully extracted significant features related to aesthetic evaluation.
  • Enabled automatic, human-free aesthetic assessment.
  • Generated measurements for mathematical aesthetic characteristics.

Conclusions:

  • The developed methods provide a framework for objective aesthetic analysis.
  • Results allow for the extrapolation of mathematical aesthetic properties.
  • Images can be located and analyzed within a defined aesthetic space.