This conference on computer image analysis in the study of art presents leading research in the application of image analysis, computer vision, and pattern recognition to problems of interest to art historians, curators and conservators. A number of recent questions and controversies have highlighted the value of rigorous image analysis in the service of the analysis of art, particularly painting. Consider these examples: the fractal image analysis for the authentication of drip paintings possibly by Jackson Pollock; sophisticated perspective, shading and form analysis to address claims that early Renaissance masters such as Jan van Eyck or Baroque masters such as Georges de la Tour traced optically projected images; automatic multi-scale analysis of brushstrokes for the attribution of portraits within a painting by Perugino; and multi-spectral, x-ray and infra-red scanning and image analysis of the Mona Lisa to reveal the painting techniques of Leonardo. The value of image analysis to these and other questions strongly suggests that current and future computer methods will play an ever larger role in the scholarship of visual arts.
We applied computational style transfer, specifically coloration and brush stroke style, to achromatic images of a ghost painting beneath Vincent van Gogh's <i>Still life with meadow flowers and roses</i>. Our method is an extension of our previous work in that it used representative artworks by the ghost painting\rq s author to train a Generalized Adversarial Network (GAN) for integrating styles learned from stylistically distinct groups of works. An effective amalgam of these learned styles is then transferred to the target achromatic work.
We discuss the problem of computationally generating images resembling those of lost cultural patrimony, specifically two-dimensional artworks such as paintings and drawings. We view the problem as one of computing an estimate of the image in the lost work that best conforms to surviving information in a variety of forms: works by the source artist, including preparatory works such as cartoons for the target work; copies of the target by other artists; other works by these artists that reveal aspects of their style; historical knowledge of art methods. and materials; stylistic conventions of the relevant era; textual descriptions of the lost work and as well as more generally, images associated with stories given by the target’s title. Some of the general information linking images and text can be learned from large corpora of natural photographs and accompanying text scraped from the web. We present some preliminary, proof-of-concept simulations for recovering lost artworks with a special focus on textual information about target artworks. We outline our future directions, such as methods for assessing the contributions of different forms of information in the overall task of recovering lost artworks.
We used deep neural network image analysis to automatically extract head pose angles—roll, yaw, pitch (or tilt)—and figure display length (quarter-length, half-length, full-length) from 11,000 digital images of portrait paintings in a wide variety of styles, from the early Renaissance through Modern eras. We tracked trends and exposed anomalies in such formal properties of these portraits, which sheds light upon the social and aesthetic forces to which portrait artists respond. For example, we find that the so-called Primitive or Naive portraitists favor a highly restricted range of pose angles (primarily frontal) while Expressionist, Mannerist, and Ukiyo-e portraitists employ a far greater range of angles. We also analyzed these formal properties to reveal the different trends throughout the careers of several individual artists, such as Paul Cezanne, Edouard Manet, and Francisco Goya. Our methods can be expanded to incorporate additional computed visual and contextual information—such as genders and ages of figures—and thus form a foundation for addressing a large range of problems in the history of art.
Similarity between faces in portraiture is incredibly informative for art historical questions involving the sitter's identity, as well for setting a painting in its historical context to understand why someone was depicted a certain way. A set of royal portraits from Song dynasty, China, has been the subject of rich art historical scholarship. Here, I demonstrate the usefulness of computer vision-based quantitative metrics in complementing existing rich subjective evaluations. Working with the portrait set, I show that L2 distances generated by OpenFace support the accepted hypothesis that emperor Lizong is depicted in Listening to the Wind in the Pines. I then use the technique to gain insight into whether the zither player in Listening to the Zither resembles emperor Huizong and why that might be, as well as what degrees of similarity between emperor portraits in the set may mean in terms of metaphorical inclusion or exclusion from the lineage. I then extend discussions on metaphorical inclusion to women in this set by exploring spousal similarity. Fascinating mysteries surrounding posthumous portraiture float amidst confounding factors in the clouds of memory, and this study shows the promise of using computer vision-based techniques as complements to subjective analyses in exploring these mysteries.