| 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. |
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@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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