Annotating shadows, highlights and faces: the contribution of a 'human in the loop' for digital art history
This work addresses the need for human-in-the-loop approaches in digital art history to complement automatic techniques, though it is incremental in nature.
The study tackled the problem of analyzing perceptual aspects in pictorial art by using human annotation tasks, such as delineating human lengths and classifying gaze direction, to replicate earlier findings and reveal novel insights, including Canaletto's accurate perspective of human figures and lateral gender bias in faces.
While automatic computational techniques appear to reveal novel insights in digital art history, a complementary approach seems to get less attention: that of human annotation. We argue and exemplify that a 'human in the loop' can reveal insights that may be difficult to detect automatically. Specifically, we focussed on perceptual aspects within pictorial art. Using rather simple annotation tasks (e.g. delineate human lengths, indicate highlights and classify gaze direction) we could both replicate earlier findings and reveal novel insights into pictorial conventions. We found that Canaletto depicted human figures in rather accurate perspective, varied viewpoint elevation between approximately 3 and 9 meters and highly preferred light directions parallel to the projection plane. Furthermore, we found that taking the averaged images of leftward looking faces reveals a woman, and for rightward looking faces showed a male, confirming earlier accounts on lateral gender bias in pictorial art. Lastly, we confirmed and refined the well-known light-from-the-left bias. Together, the annotations, analyses and results exemplify how human annotation can contribute and complement to technical and digital art history.