Quantifying Aesthetics of Visual Design Applied to Automatic Design

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Springer, Jun 20, 2016 - Technology & Engineering - 137 pages

In this thesis, the author makes several contributions to the study of design of graphical materials. The thesis begins with a review of the relationship between design and aesthetics, and the use of mathematical models to capture this relationship. Then, a novel method for linking linguistic concepts to colors using the Latent Dirichlet Allocation Dual Topic Model is proposed. Next, the thesis studies the relationship between aesthetics and spatial layout by formalizing the notion of visual balance. Applying principles of salience and Gaussian mixture models over a body of about 120,000 aesthetically rated professional photographs, the author provides confirmation of Arnhem's theory about spatial layout. The thesis concludes with a description of tools to support automatically generating personalized design.


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1 Introduction
2 On the Legitimacy of Quantifying Aesthetics
3 Design Mining Color Semantics
4 Design Mining Visual Balance
A Case Study of Magazine Covers
6 Recommendation System for Automatic Design
A Design Mining Color Semantics
B Derivations of Gibbs Sampling for LDADual

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About the author (2016)

Dr. Ali Jahanian graduated with a Ph.D. from the School of Electrical and Computer Engineering at Purdue University, and is currently at the Massachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory.