Capturing the Flow of Art History

TL;DR

This paper investigates how machine learning models can understand and interpret the evolution and flow of art styles over history, focusing on neural networks and manifold learning techniques.

Contribution

It explores the application of advanced neural networks and manifold learning to analyze and visualize the transition and flow of art styles over time.

Findings

Neural networks can effectively model art style features.

Manifold learning reveals the evolution of art styles.

The approach offers new insights into art history analysis.

Abstract

Do we really understand how machine classifies art styles? Historically, art is perceived and interpreted by human eyes and there are always controversial discussions over how people identify and understand art. Historians and general public tend to interpret the subject matter of art through the context of history and social factors. Style, however, is different from subject matter. Given the fact that Style does not correspond to the existence of certain objects in the painting and is mainly related to the form and can be correlated with features at different levels.(Ahmed Elgammal et al. 2018), which makes the identification and classification of the characteristics artwork's style and the "transition" - how it flows and evolves - remains as a challenge for both human and machine. In this work, a series of state-of-art neural networks and manifold learning algorithms are explored to unveil this intriguing topic: How does machine capture and interpret the flow of Art History?