# Linking Art through Human Poses

## TL;DR

This paper introduces a method for linking artworks based on human pose similarity, improving the detection of composition transfer in art collections, which aids art history and cultural preservation.

## Contribution

The paper presents a novel approach that uses human pose matching to connect artworks, outperforming traditional image retrieval methods in identifying composition transfer.

## Key findings

- Pose matching outperforms standard content-based retrieval.
- Explicit human pose analysis improves art linkage accuracy.
- Method is effective on a manually annotated dataset.

## Abstract

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.