Toward Quantifying Ambiguities in Artistic Images
This addresses the challenge of quantifying ambiguities in aesthetic experiences for researchers in art and perception, though it is incremental as it builds on existing theories with new methods.
The paper tackled the problem of measuring perceptual ambiguity in artistic images by developing an approach using crowdworker descriptions and text processing, showing it can provide fine-grained measurements of image ambiguities.
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 paper 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.