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Measuring the naturalness of images is important to generate realistic imagesor to detect unnatural regions in images. Additionally, a method to measurenaturalness can be complementary to Convolutional Neural Network (CNN) basedfeatures, which are known to be insensitive to the naturalness of images.However, most probabilistic image models have insufficient capability ofmodeling the complex and abstract naturalness that we feel because they arebuilt directly on raw image pixels. In this work, we assume that naturalnesscan be measured by the predictability on high-level features during eyemovement. Based on this assumption, we propose a novel method to evaluate thenaturalness by building a variant of Recurrent Neural Network Language Modelson pre-trained CNN representations. Our method is applied to two tasks,demonstrating that 1) using our method as a regularizer enables us to generatemore understandable images from image features than existing approaches, and 2)unnaturalness maps produced by our method achieve state-of-the-art eye fixationprediction performance on two well-studied datasets.

Visual Language Modeling on CNN Image Representations