Paintings and Drawings Aesthetics Assessment with Rich Attributes for Various Artistic Categories

TL;DR

This paper introduces a comprehensive dataset and a novel neural network approach for evaluating aesthetic qualities of paintings and drawings across multiple categories and attributes, addressing a gap in art-specific aesthetic assessment research.

Contribution

The creation of the APDD dataset with multi-attribute annotations and the development of the AANSPS model for aesthetic evaluation in diverse artistic styles.

Findings

APDD contains 4985 images with over 31,100 annotations.

AANSPS effectively assesses aesthetic attributes across various painting styles.

The dataset and model advance aesthetic evaluation in art beyond photographic domains.

Abstract

Image aesthetic evaluation is a highly prominent research domain in the field of computer vision. In recent years, there has been a proliferation of datasets and corresponding evaluation methodologies for assessing the aesthetic quality of photographic works, leading to the establishment of a relatively mature research environment. However, in contrast to the extensive research in photographic aesthetics, the field of aesthetic evaluation for paintings and Drawings has seen limited attention until the introduction of the BAID dataset in March 2023. This dataset solely comprises overall scores for high-quality artistic images. Our research marks the pioneering introduction of a multi-attribute, multi-category dataset specifically tailored to the field of painting: Aesthetics of Paintings and Drawings Dataset (APDD). The construction of APDD received active participation from 28 professional artists worldwide, along with dozens of students specializing in the field of art. This dataset encompasses 24 distinct artistic categories and 10 different aesthetic attributes. Each image in APDD has been evaluated by six professionally trained experts in the field of art, including assessments for both total aesthetic scores and aesthetic attribute scores. The final APDD dataset comprises a total of 4985 images, with an annotation count exceeding 31100 entries. Concurrently, we propose an innovative approach: Art Assessment Network for Specific Painting Styles (AANSPS), designed for the assessment of aesthetic attributes in mixed-attribute art datasets. Through this research, our goal is to catalyze advancements in the field of aesthetic evaluation for paintings and drawings, while enriching the available resources and methodologies for its further development and application.