It has long been hypothesized that perceptual ambiguities play an important role in aesthetic experience: A work with some ambiguity engages a viewer more than one that does not. However, current frameworks for testing this theory are limited by the availability of stimuli and data collection methods. This article presents an approach to measuring the perceptual ambiguity of a collection of images. Crowdworkers are asked to describe image content, after different viewing durations. Experiments are performed using images created with Generative Adversarial Networks, using the Artbreeder website. We show that text processing of viewer responses can provide a fine-grained way to measure and describe image ambiguities.

中文翻译：

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量化艺术图像中的模糊性

长期以来，人们一直假设感知模糊性在审美体验中起着重要作用：带有模糊性的作品比没有模糊性的作品更能吸引观众。然而，当前测试该理论的框架受到刺激和数据收集方法的可用性的限制。本文介绍了一种测量图像集合的感知模糊度的方法。在不同的观看时间之后，要求众包工作人员描述图像内容。使用 Artbreeder 网站使用生成对抗网络创建的图像进行实验。我们表明，查看者响应的文本处理可以提供一种细粒度的方法来测量和描述图像的模糊性。