Detecting Visual Design Principles in Art and Architecture through Deep Convolutional Neural Networks
This work addresses the challenge of objective aesthetic evaluation in design for artists, architects, and designers, but it is incremental as it applies existing deep learning methods to a new domain.
The researchers tackled the problem of numerically analyzing visual design principles in art and architecture, which is typically subjective, by developing a deep convolutional neural network model that classifies these principles across domains like artwork, photos, and building facades, using a computationally-based synthetic dataset.
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.