Quantifying aesthetics of visual design applied to automatic design

<p>In today's "Instagram" world, with advances in ubiquitous computing and access to social networks, digital media is adopted by art and culture. In this dissertation, we study what makes a good design by investigating mechanisms to bring aesthetics of design from realm of subjection to objection. These mechanisms are a combination of three main approaches: learning theories and principles of design by collaborating with professional designers, mathematically and statistically modeling good designs from large scale datasets, and crowdscourcing to model perceived aesthetics of designs from general public responses. We then apply the knowledge gained in automatic design creation tools to help non-designers in self-publishing, and designers in inspiration and creativity. Arguably, unlike visual arts where the main goals may be abstract, visual design is conceptualized and created to convey a message and communicate with audiences. Therefore, we develop a semantic design mining framework to automatically link the design elements, layout, color, typography, and photos to linguistic concepts. The inferred semantics are applied to a design expert system to leverage user interactions in order to create personalized designs via recommendation algorithms based on the user's preferences.</p>

Name thesis:degree_name

Doctor of Philosophy (PhD)

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Electrical and Computer Engineering

Year

2014

Author dc:creator

• Jahanian, Ali

Contributors dc:contributor

• Jan P. Allebach
• Mireille Boutin
• S.V.N. Vishantathan
• M J T Smith
• Ruth Rosenholtz

• Electrical and Computer Engineering
• Electrical and Electronics

Repository record dc:identifier

https://docs.lib.purdue.edu/open_access_dissertations/295

OAI identifier oai:identifier

oai:docs.lib.purdue.edu:open_access_dissertations-1364