The Automatic Annotation and Retrieval of Digital Images of Prints and Tile Panels using Network Link Analysis Algorithms

The study of the visual art of printmaking is fundamental for art history. Printmaking methods have been used for centuries to replicate visual art works, which have influenced generations of artists. Particularly in this work, we are interested in the influence of prints on artistic tile panel painters, who have produced an impressive body of work in Portugal. The study of such panels has gained interest by art historians, who try to understand the influence of prints on tile panels artists in order to understand the evolution of this type of visual arts. Several databases of digitized art images have been used for such end, but the use of these databases relies on manual image annotations, an effective internal organization, and an ability of the art historian to visually recognize relevant prints. We propose an automation of these tasks using statistical pattern recognition techniques that takes into account not only the manual annotations available, but also the visual characteristics of the images. Specifically, we introduce a new network link-analysis method for the automatic annotation and retrieval of digital images of prints. Using a database of 307 annotated images of prints, we show that the annotation and retrieval results produced by our approach are better than the results of state-of-the-art content-based image retrieval methods.