  We address the discovery of composition transfer in artworks based on their
visual content. Automated analysis of large art collections, which are growing
as a result of art digitization among museums and galleries, is an important
tool for art history and assists cultural heritage preservation. Modern image
retrieval systems offer good performance on visually similar artworks, but fail
in the cases of more abstract composition transfer. The proposed approach links
artworks through a pose similarity of human figures depicted in images. Human
figures are the subject of a large fraction of visual art from middle ages to
modernity and their distinctive poses were often a source of inspiration among
artists. The method consists of two steps -- fast pose matching and robust
spatial verification. We experimentally show that explicit human pose matching
is superior to standard content-based image retrieval methods on a manually
annotated art composition transfer dataset.
