On the Expressiveness of Line Drawings

TL;DR

This paper investigates whether a neural network can quantify the expressiveness of line drawings by analyzing line attributes related to viewing times, demonstrating that expressiveness can be computationally detected.

Contribution

It introduces a method using neural networks and support vector machines to classify line drawings based on kinematic and diffusion models linked to viewing times.

Findings

Neural networks can detect expressiveness in line drawings.

Extreme viewing times correlate with perceived expressiveness.

The approach provides a computational measure of artistic expressiveness.

Abstract

Can expressiveness of a drawing be traced with a computer? In this study a neural network (perceptron) and a support vector machine are used to classify line drawings. To do this the line drawings are attributed values according to a kinematic model and a diffusion model for the lines they consist of. The values for both models are related to looking times. Extreme values according to these models, that is both extremely short and extremely long looking times, are interpreted as indicating expressiveness. The results strongly indicate that expressiveness in this sense can be detected, at least with a neural network.