Visual Language Modeling on CNN Image Representations

Hiroharu Kato; Tatsuya Harada

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arXiv · 1511.02872

Visual Language Modeling on CNN Image Representations

Hiroharu Kato·Tatsuya Harada

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Abstract

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

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BibTeX RIS

Hiroharu Kato, Tatsuya Harada. 2015-11-09. Visual Language Modeling on CNN Image Representations. https://arxiv.org/abs/1511.02872

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