Towards Efficient Human Action Retrieval based on Triplet-Loss
Metric Learning

D 2022

Towards Efficient Human Action Retrieval based on Triplet-Loss Metric Learning

KICO, Iris, Jan SEDMIDUBSKÝ a Pavel ZEZULA

Základní údaje

Originální název

Towards Efficient Human Action Retrieval based on Triplet-Loss Metric Learning

Autoři

KICO, Iris (70 Bosna a Hercegovina, domácí), Jan SEDMIDUBSKÝ (203 Česká republika, garant, domácí) a Pavel ZEZULA (203 Česká republika, domácí)

Vydání

Berlin, Heidelberg, 33rd International Conference on Database and Expert Systems Applications (DEXA), od s. 234-247, 14 s. 2022

Nakladatel

Springer-Verlag

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10200 1.2 Computer and information sciences

Stát vydavatele

Spojené státy

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

elektronická verze "online"

Odkazy

URL

Impakt faktor

Impact factor: 0.402 v roce 2005

Kód RIV

RIV/00216224:14330/22:00125807

Organizační jednotka

Fakulta informatiky

ISBN

978-3-031-12422-8

ISSN

DOI

http://dx.doi.org/10.1007/978-3-031-12423-5_18

UT WoS

000877013800018

Klíčová slova anglicky

human motion data;skeleton sequences;action similarity;action retrieval;triplet-loss learning;LSTM

Štítky

core_B, firank_B

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 28. 3. 2023 10:50, RNDr. Pavel Šmerk, Ph.D.

Anotace

V originále

Recent pose-estimation methods enable digitization of human motion by extracting 3D skeleton sequences from ordinary video recordings. Such spatio-temporal skeleton representation offers attractive possibilities for a wide range of applications but, at the same time, requires effective and efficient content-based access to make the extracted data reusable. In this paper, we focus on content-based retrieval of pre-segmented skeleton sequences of human actions to identify the most similar ones to a query action. We mainly deal with the extraction of content-preserving action features, which are learned using the triplet-loss approach in an unsupervised way. Such features are (1) effective as they achieve a similar retrieval quality as the features learned in a supervised way, and (2) of a fixed size which enables the application of indexing structures for efficient retrieval.

Návaznosti

EF16_019/0000822, projekt VaV

Název: Centrum excelence pro kyberkriminalitu, kyberbezpečnost a ochranu kritických informačních infrastruktur

MUNI/A/1195/2021, interní kód MU

Název: Aplikovaný výzkum v oblastech vyhledávání, analýz a vizualizací rozsáhlých dat, zpracování přirozeného jazyka a aplikované umělé inteligence

Investor: Masarykova univerzita, Aplikovaný výzkum v oblastech vyhledávání, analýz a vizualizací rozsáhlých dat, zpracování přirozeného jazyka a aplikované umělé inteligence

Citovat

KICO, Iris, Jan SEDMIDUBSKÝ a Pavel ZEZULA. Towards Efficient Human Action Retrieval based on Triplet-Loss Metric Learning. Online. In Strauss, C., Cuzzocrea, A., Kotsis, G., Tjoa, A.M., Khalil, I. 33rd International Conference on Database and Expert Systems Applications (DEXA). Berlin, Heidelberg: Springer-Verlag, 2022, s. 234-247. ISBN 978-3-031-12422-8. Dostupné z: https://dx.doi.org/10.1007/978-3-031-12423-5_18.

@inproceedings{1852909,
   author = {Kico, Iris and Sedmidubský, Jan and Zezula, Pavel},
   address = {Berlin, Heidelberg},
   booktitle = {33rd International Conference on Database and Expert Systems Applications (DEXA)},
   doi = {http://dx.doi.org/10.1007/978-3-031-12423-5_18},
   editor = {Strauss, C., Cuzzocrea, A., Kotsis, G., Tjoa, A.M., Khalil, I.},
   keywords = {human motion data;skeleton sequences;action similarity;action retrieval;triplet-loss learning;LSTM},
   howpublished = {elektronická verze "online"},
   language = {eng},
   location = {Berlin, Heidelberg},
   isbn = {978-3-031-12422-8},
   pages = {234-247},
   publisher = {Springer-Verlag},
   title = {Towards Efficient Human Action Retrieval based on Triplet-Loss Metric Learning},
   url = {https://link.springer.com/chapter/10.1007/978-3-031-12423-5_18},
   year = {2022}
}

TY  - JOUR
ID  - 1852909
AU  - Kico, Iris - Sedmidubský, Jan - Zezula, Pavel
PY  - 2022
TI  - Towards Efficient Human Action Retrieval based on Triplet-Loss Metric Learning
PB  - Springer-Verlag
CY  - Berlin, Heidelberg
SN  - 9783031124228
KW  - human motion data;skeleton sequences;action similarity;action retrieval;triplet-loss learning;LSTM
UR  - https://link.springer.com/chapter/10.1007/978-3-031-12423-5_18
N2  - Recent pose-estimation methods enable digitization of human motion by extracting 3D skeleton sequences from ordinary video recordings. Such spatio-temporal skeleton representation offers attractive possibilities for a wide range of applications but, at the same time, requires effective and efficient content-based access to make the extracted data reusable. In this paper, we focus on content-based retrieval of pre-segmented skeleton sequences of human actions to identify the most similar ones to a query action. We mainly deal with the extraction of content-preserving action features, which are learned using the triplet-loss approach in an unsupervised way. Such features are (1) effective as they achieve a similar retrieval quality as the features learned in a supervised way, and (2) of a fixed size which enables the application of indexing structures for efficient retrieval.
ER  -

KICO, Iris, Jan SEDMIDUBSKÝ a Pavel ZEZULA. Towards Efficient Human Action Retrieval based on Triplet-Loss Metric Learning. Online. In Strauss, C., Cuzzocrea, A., Kotsis, G., Tjoa, A.M., Khalil, I. \textit{33rd International Conference on Database and Expert Systems Applications (DEXA)}. Berlin, Heidelberg: Springer-Verlag, 2022, s.~234-247. ISBN~978-3-031-12422-8. Dostupné z: https://dx.doi.org/10.1007/978-3-031-12423-5\_{}18.

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