Abstract
Recent triumphs and future opportunities for computer-assisted connoisseurship of fine art paintings and drawings. Our cultural patrimony of fine art paintings and drawings comprises some of the most important, memorable, and consequential images ever created, and presents numerous problems in art history and the interpretation of “authored” stylized images. While sophisticated imaging (by numerous methods) has long been a mainstay in museum curation and conservation, it is only in the past few years that true image analysis—powered by computer vision, machine learning, computer graphics, and artificial intelligence (AI)—have been applied to fine art images. Fine art paintings differ in numerous ways from the traditional photographs, videos, and medical images that have commanded the attention of most experts up to now: such paintings vary extensively in style, content, non-realistic conventions, and especially intended meaning. Rigorous computer methods have outperformed even seasoned connoisseurs on several tasks in the image understanding of art and have provided new insights and settled deep disputes in art history. Additionally, the classes of problems in art analysis, particularly those centered on inferring meaning from images, are forcing computer experts to develop new algorithms and concepts in AI. This talk, profusely illustrated with fine art images and computer analyses, argues for the new discipline of computer-assisted connoisseurship, a merger of humanist and scientific approaches to image understanding. Such work will continue to be embraced by art scholars and address new grand challenges in AI.
Citation: Proceedings of the 3rd International Conference on Artificial Intelligence and Machine Learning (AIM – 2026). USG Proc J 2(Suppl 17): S1-S25.
