Can machine vision detect unknown connections between famous or classic artworks? This seems to be the case, as the work of Tomas Jenicek and Ondřej Chum shows. In their paper “Linking Art through Human Poses” they write: “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.” (Abstract) Human figures are the subject of many paintings from the Middle Ages to the Modern Age and their unmistakable poses have often been a source of inspiration among artists. Think of “Sleeping Venus” by Giorgione, “Venus of Urbino” by Titian and “Olympia” by Édouard Manet – paintings with similarities and correlations. The method of the two scientists consists of fast pose matching and robust spatial verification. They “experimentally show that explicit human pose matching is superior to standard content-based image retrieval methods on a manually annotated art composition transfer dataset” (Abstract). The paper can be downloaded via arxiv.org/abs/1907.03537.