Linking Art through Human Poses
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Linking Art through Human Poses
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/GA19-23165S" target="_blank" >GA19-23165S: Generalized Image Retrieval and Relation Discovery</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
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
Number of pages
8
Pages from-to
1338-1345
Publisher name
IEEE
Place of publication
Piscataway, NJ
Event location
Sydney
Event date
Sep 20, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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