Linking Art through Human Poses

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F19%3A00335543" target="_blank" >RIV/68407700:21230/19:00335543 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1109/ICDAR.2019.00216" target="_blank" >https://doi.org/10.1109/ICDAR.2019.00216</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICDAR.2019.00216" target="_blank" >10.1109/ICDAR.2019.00216</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Linking Art through Human Poses

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Linking Art through Human Poses

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/GA19-23165S" target="_blank" >GA19-23165S: Zobecněné vyhledávání obrázků a objevování relací</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

ICDAR2019: Proceedings of the 15th IAPR International Conference on Document Analysis and Recognition

ISBN

978-1-7281-3015-6

ISSN

1520-5363

e-ISSN

2379-2140

Počet stran výsledku

8

Strana od-do

1338-1345

Název nakladatele

IEEE

Místo vydání

Piscataway, NJ

Místo konání akce

Sydney

Datum konání akce

20. 9. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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