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：视觉刺激的复杂度在许多认知现象中扮演着重要角色，包括注意力、参与度、记忆性、时间感知和审美评价。尽管其重要性不言而喻，但人们对复杂度的理解仍较为有限，且讽刺的是，以往的图像复杂度模型往往本身相当复杂。研究者曾多次尝试寻找能够解释复杂度的人工设计特征，但这些特征通常局限于特定数据集，因此难以泛化。另一方面，近期研究采用深度神经网络来预测复杂度，但这些模型仍难以解释，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割表示来建模复杂度。我们利用最先进的SAM和FC-CLIP分割模型，分别量化图像在多个粒度下的分割段数量以及类别数量。研究发现，在包含自然场景和艺术图像的六个多样化图像数据集中，一个仅包含这两个特征的简单线性模型能够很好地解释复杂度。这表明图像的复杂度可能出乎意料地简单。