Computational Aesthetic Evaluation of Logos

Abstract

Computational aesthetics has become an active research field in recent years, but there have been few attempts in computational aesthetic evaluation of logos. In this article, we restrict our study on black-and-white logos, which are professionally designed for name-brand companies with similar properties, and apply perceptual models of standard design principles in computational aesthetic evaluation of logos. We define a group of metrics to evaluate some aspects in design principles such as balance, contrast, and harmony of logos. We also collect human ratings of balance, contrast, harmony, and aesthetics of 60 logos from 60 volunteers. Statistical linear regression models are trained on this database using a supervised machine-learning method. Experimental results show that our model-evaluated balance, contrast, and harmony have highly significant correlation of over 0.87 with human evaluations on the same dimensions. Finally, we regress human-evaluated aesthetics scores on model-evaluated balance, contrast, and harmony. The resulted regression model of aesthetics can predict human judgments on perceived aesthetics with a high correlation of 0.85. Our work provides a machine-learning-based reference framework for quantitative aesthetic evaluation of graphic design patterns and also the research of exploring the relationship between aesthetic perceptions of human and computational evaluation of design principles extracted from graphic designs.