Detecting Visual Design Principles in Art and Architecture through Deep Convolutional Neural Networks

TL;DR

This paper presents a deep learning model that automatically recognizes and classifies visual design principles across art, photography, and architecture, aiming to objectify aesthetic evaluation.

Contribution

It introduces a neural network approach trained on diverse datasets, including synthetic data, to identify design principles in various visual domains.

Findings

Model successfully classifies design principles across domains

Synthetic dataset enhances model learning

Provides objective aesthetic evaluation

Abstract

Visual design is associated with the use of some basic design elements and principles. Those are applied by the designers in the various disciplines for aesthetic purposes, relying on an intuitive and subjective process. Thus, numerical analysis of design visuals and disclosure of the aesthetic value embedded in them are considered as hard. However, it has become possible with emerging artificial intelligence technologies. This research aims at a neural network model, which recognizes and classifies the design principles over different domains. The domains include artwork produced since the late 20th century; professional photos; and facade pictures of contemporary buildings. The data collection and curation processes, including the production of computationally-based synthetic dataset, is genuine. The proposed model learns from the knowledge of myriads of original designs, by capturing the underlying shared patterns. It is expected to consolidate design processes by providing an aesthetic evaluation of the visual compositions with objectivity.