EVA: An Explainable Visual Aesthetics Dataset

Abstract

Assessing visual aesthetics has important applications in several domains, from image retrieval and recommendation to enhancement. Modern aesthetic quality predictors are data driven, and leverage the availability of large annotated datasets to train accurate models. However, labels in existing datasets are often noisy, incomplete or they do not allow more sophisticated tasks such as understanding why an image looks beautiful or not to a human observer. In this paper, we propose an Explainable Visual Aesthetics (EVA) dataset, which contains 4070 images with at least 30 votes per image. Compared to previous datasets, EVA has been crowdsourced using a more disciplined approach inspired by quality assessment best practices. It also offers additional features, such as the degree of difficulty in assessing the aesthetic score, rating for 4 complementary aesthetic attributes, as well as the relative importance of each attribute to form aesthetic opinions. A statistical analysis on EVA demonstrates that the collected attributes and relative importance can be linearly combined to explain effectively the overall aesthetic mean opinion scores. The dataset, made publicly available, is expected to contribute to future research on understanding and predicting visual quality aesthetics.