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. |
Další formáty:
BibTeX
LaTeX
RIS
@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.
|