Beyond Saliency: Assessing Visual Balance with High-level Cues
Baris Kandemir, Zihan Zhou, Jia Li, James Z. Wang
The Pennsylvania State University, USA
Abstract:
Automatic composition optimization is a vital technique for computational
photography systems. Balance in composition is one of the
agreed-upon principles of aesthetics and is commonly employed
as a visual feature in many computational aesthetics studies. It
refers to an equilibrium of visual weights within composition. Existing
composition optimization and aesthetic quality assessment
systems utilize the saliency map to represent balance. However,
saliency map methods fail to account for high-level visual features
that are important for compositional balance. Our work establishes
a framework for the purpose of evaluating the relationship between
visual features and compositional balance. This provides a better
understanding of compositional balance and help improve composition
optimization performance. A dataset based on a human
subject study was created with photos representing main balance
concepts such as symmetric, dynamic balance, and imbalance. We
take the visual center given by human subjects as the dependent
variable and the center-of-mass for each type of visual features
as the predictor variable. Based on a linear regression model, we
can assess how much each type of visual features contributes to
the prediction of the visual center. Our findings show that highlevel
visual elements can help increase prediction accuracy with
significance on top of saliency maps. Specifically, extra information
provided through human and dominant vanishing point detection
is statistically significant for assessing balance in the composition.
Full color PDF file (10.5 MB)
Citation:
Baris Kandemir, Zihan Zhou, Jia Li and James Z. Wang, ``Beyond
Saliency: Assessing Visual Balance with High-level Cues,'' Proceedings
of the Thematic Workshops of ACM Multimedia, in conjunction with the
ACM Multimedia Conference, pp. 26-34 , Mountain View, California,
October 2017.
Copyright 2017 ACM. Personal use of this material is
permitted. However, permission to reprint/republish this material for
advertising or promotional purposes or for creating new collective
works for resale or redistribution to servers or lists, or to reuse
any copyrighted component of this work in other works, must be
obtained from ACM.
Last Modified:
August 10, 2017
© 2017