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 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.

翻译：视觉刺激的复杂性在许多认知现象中扮演着重要角色，包括注意力、参与度、可记忆性、时间感知和审美评价。尽管重要性突出，但人们对复杂性的理解仍相当有限，且具有讽刺意味的是，以往的图像复杂性模型本身颇为复杂。学界曾多次尝试寻找能解释复杂性的手工特征，但这些特征通常局限于特定数据集，因而缺乏泛化能力。另一方面，近期研究采用深度神经网络来预测复杂性，但这些模型难以解释，且未能为问题的理论理解提供指导。在此，我们提出基于图像的分段表示来建模复杂性。我们利用最先进的分割模型SAM和FC-CLIP，分别量化图像在多个粒度下的分段数量以及类别数量。研究发现，在涵盖自然场景与艺术图像的六组多样化图像数据集中，仅凭这两个特征构建的简单线性模型便能很好地解释复杂性。这表明，图像的复杂性可能出乎意料地简单。