Talk · IEEE International Conference on Smart Information Systems and Technologies · May 14, 2025

Aesthetic Index for Art Paintings Using Visual Features

Yerdauit Torekhan, Nurdaulet Altynbekov, Pakizar Shamoi

Abstract

In the digital era, assessing image aesthetics has become essential for various applications, including photography ranking, content recommendation, and human-computer interaction. However, due to the subjective nature of human perception, defining and quantifying aesthetics remains a challenging task in computational aesthetics. This paper introduces a novel approach to evaluating the aesthetic quality of paintings based on three fundamental features: symmetry, simplicity, and color harmony. By applying computational techniques to measure these attributes, we propose an aesthetic index that provides an objective assessment of artistic composition. We conduct an experiment on a large-scale dataset of paintings, "Best Artworks of All Time," to analyze the effectiveness of our method. The results reveal that Kazimir Malevich, Paul Gauguin, and Joan Miró are among the artists with the highest average aesthetic scores. Additionally, we identify the top 10 most aesthetically pleasing artworks, with paintings from Malevich, Rublev, and Klee ranking among the highest. The average computed aesthetic score across all artworks is 71.74. Our results show that basic visual characteristics can be used to measure a painting’s aesthetic appeal. The paper methodology offers a means of assessing and contrasting artworks that can be used to build aesthetic composition trends.

Aesthetic Perception and AnalysisImage Retrieval and Classification TechniquesImage and Video Quality Assessmentcomputational aestheticsaesthetics of paintingsymmetry

Aesthetic Index for Art Paintings Using Visual Features

Abstract

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