LAPIS: A novel dataset for personalized image aesthetic assessment

Anne-Sofie Maerten, Li-Wei Chen, Stefanie De Winter, Christophe Bossens, Johan Wagemans

Keywords: dataset, computational aesthetics, personalized image aesthetic assessment

TL;DR: We present the first artistic dataset for Personalized Image Aesthetic Assessment (PIAA) called LAPIS, which is meticulously curated and offers rich personal and image attributes.

Abstract: We present the Large Art Personalized Image Set (LAPIS), a novel dataset for personalized image aesthetic assessment (PIAA). It is the first dataset with images of artworks that is suitable for PIAA. LAPIS consists of 11,723 images and was meticulously curated in collaboration with art historians. Each image has an aesthetics score and a set of image attributes known to relate to aesthetic appreciation. Besides rich image attributes, LAPIS offers rich personal attributes of each annotator. We implemented two existing state-of-the-art PIAA models and assessed their performance on LAPIS. We assess the contribution of personal attributes and image attributes through ablation studies and find that performance deteriorates when certain personal and image attributes are removed. An analysis of failure cases reveals that both existing models make similar incorrect predictions, highlighting the need for improvements in artistic image aesthetic assessment.

Submission Number: 24