Understanding Aesthetics with Language: A Photo Critique Dataset for Aesthetic Assessment

• URL: http://arxiv.org/abs/2206.08614v1
• Date: Fri, 17 Jun 2022 08:16:20 GMT
• Title: Understanding Aesthetics with Language: A Photo Critique Dataset for Aesthetic Assessment
• Authors: Daniel Vera Nieto and Luigi Celona and Clara Fernandez-Labrador
• Abstract summary: We propose the Critique Photo Reddit dataset (RPCD), which contains 74K images and 220K comments. We exploit the polarity of the sentiment of criticism as an indicator of aesthetic judgment.
• Score: 6.201485014848172
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Computational inference of aesthetics is an ill-defined task due to its subjective nature. Many datasets have been proposed to tackle the problem by providing pairs of images and aesthetic scores based on human ratings. However, humans are better at expressing their opinion, taste, and emotions by means of language rather than summarizing them in a single number. In fact, photo critiques provide much richer information as they reveal how and why users rate the aesthetics of visual stimuli. In this regard, we propose the Reddit Photo Critique Dataset (RPCD), which contains tuples of image and photo critiques. RPCD consists of 74K images and 220K comments and is collected from a Reddit community used by hobbyists and professional photographers to improve their photography skills by leveraging constructive community feedback. The proposed dataset differs from previous aesthetics datasets mainly in three aspects, namely (i) the large scale of the dataset and the extension of the comments criticizing different aspects of the image, (ii) it contains mostly UltraHD images, and (iii) it can easily be extended to new data as it is collected through an automatic pipeline. To the best of our knowledge, in this work, we propose the first attempt to estimate the aesthetic quality of visual stimuli from the critiques. To this end, we exploit the polarity of the sentiment of criticism as an indicator of aesthetic judgment. We demonstrate how sentiment polarity correlates positively with the aesthetic judgment available for two aesthetic assessment benchmarks. Finally, we experiment with several models by using the sentiment scores as a target for ranking images. Dataset and baselines are available (https://github.com/mediatechnologycenter/aestheval).

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