Investigating the diversity and stylization of contemporary user generated visual arts in the complexity entropy plane
Seunghwan Kim, Byunghwee Lee, Wonjae Lee

TL;DR
This study uses the complexity-entropy plane to analyze the evolution and diversity of contemporary user-generated visual arts, revealing statistical relationships between local image structures and artistic styles over a decade.
Contribution
It introduces a physics-inspired, machine learning-based approach to quantitatively map the evolution and stylistic diversity of visual arts in the C-H plane.
Findings
Significant correlation between C-H information and multi-level image features.
Identification of a C-H region with high stylistic diversity.
Empirical evidence of emerging novel art styles over time.
Abstract
The advent of computational and numerical methods in recent times has provided new avenues for analyzing art historiographical narratives and tracing the evolution of art styles therein. Here, we investigate an evolutionary process underpinning the emergence and stylization of contemporary user-generated visual art styles using the complexity-entropy (C-H) plane, which quantifies local structures in paintings. Informatizing 149,780 images curated in DeviantArt and Behance platforms from 2010 to 2020, we analyze the relationship between local information of the C-H space and multi-level image features generated by a deep neural network and a feature extraction algorithm. The results reveal significant statistical relationships between the C-H information of visual artistic styles and the dissimilarities of the multi-level image features over time within groups of artworks. By disclosing…
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Taxonomy
TopicsDigital Media and Visual Art · Creativity in Education and Neuroscience
