Simplicity in Complexity
Kevin Shen Surabhi S Nath Aenne Brielmann Peter Dayan
The complexity of visual stimuli plays an important role in many cognitive
phenomena, including attention, engagement, memorability, time perception and
aesthetic evaluation. Despite its importance, complexity is poorly understood
and ironically, previous models of image complexity have been quite
\textit{complex}. There have been many attempts to find handcrafted features
that explain complexity, but these features are usually dataset specific, and
hence fail to generalise. On the other hand, more recent work has employed deep
neural networks to predict complexity, but these models remain difficult to
interpret, and do not guide a theoretical understanding of the problem. Here we
propose to model complexity using segment-based representations of images. We
use state-of-the-art segmentation models, SAM and FC-CLIP, to quantify the
number of segments at multiple granularities, and the number of classes in an
image respectively. We find that complexity is well-explained by a simple
linear model with these two features across six diverse image-sets of
naturalistic scene and art images. This suggests that the complexity of images
can be surprisingly simple.