Speaker: David G. Stork, Stanford Statistics
Abstract: Computer vision and statistics, including deep networks, have led to numerous successes in the analysis of natural photographs, medical, remotely sensed, and other images that conform to the optics, physics, and statistics of natural scenes. Fine art paintings and drawings differ in important ways from such images, however, they can be highly stylized, depict non-existent scenes, objects, or even no objects at all, violate traditional physical constraints on images such as perspective, express an artist's intention or meaning, and are far fewer in number than the photographs used to train traditional image analysis algorithms. For these and additional reasons, the computational analysis of fine art requires modification of prior analysis techniques and even entirely new technical approaches. As such, fine art presents a grand challenge to artificial intelligence research.
This talk will present some of the recent success of rigorous statistical and computer vision methods applied to problems in the history and interpretation of fine art paintings and drawings, including exposing fakes and forgeries. It will discuss initial steps toward using statistical methods in the analysis of both images and associated text to compute simple interpretations of the meanings of some artworks.
This profusely illustrated talk will end with a list of several open problems in statistical image analysis of our cultural patrimony, including some of the most important images ever created.