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Indexing and retrieval of human motion data by a hierarchical tree

Authors:

Shihong XiaAuthors Info & Claims

VRST '09: Proceedings of the 16th ACM Symposium on Virtual Reality Software and Technology

Pages 207 - 214

https://doi.org/10.1145/1643928.1643974

Published: 18 November 2009 Publication History

Abstract

For the convenient reuse of large-scale 3D motion capture data, browsing and searching methods for the data should be explored. In this paper, an efficient indexing and retrieval approach for human motion data is presented based on a novel similarity metric. We divide the human character model into three partitions to reduce the spatial complexity and measure the temporal similarity of each partition by self-organizing map and Smith--Waterman algorithm. The overall similarity between two motion clips can be achieved by integrating the similarities of the separate body partitions. Then the hierarchical clustering method is implemented, which can not only cluster the motion data accurately, but also discover the relationships between different motion types by a binary tree structure. With our typical cluster locating algorithm and motion motif mining method, fast and accurate retrieval can be performed. The experiment results show the effectiveness of our approach.

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Cited By

Tang MHuang TLuo JLi X(2022)PAMS-DP: Building a Unified Open PAMS Human Movement Data Platform2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)10.1109/BIBM55620.2022.9995069(2474-2481)Online publication date: 6-Dec-2022
https://doi.org/10.1109/BIBM55620.2022.9995069
Budikova PSedmidubsky JZezula PCheng WKankanhalli MWang MChu WLiu JWorring M(2021)Efficient Indexing of 3D Human MotionsProceedings of the 2021 International Conference on Multimedia Retrieval10.1145/3460426.3463646(10-18)Online publication date: 24-Aug-2021
https://dl.acm.org/doi/10.1145/3460426.3463646
Sedmidubsky JElias PBudikova PZezula P(2021)Content-Based Management of Human Motion Data: Survey and ChallengesIEEE Access10.1109/ACCESS.2021.30757669(64241-64255)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3075766
Show More Cited By

Index Terms

Indexing and retrieval of human motion data by a hierarchical tree
1. Computing methodologies
  1. Computer graphics
    1. Animation
2. Information systems
  1. Information retrieval
  2. Information systems applications
    1. Data mining
      1. Clustering

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Information & Contributors

Information

Published In

cover image ACM Conferences

VRST '09: Proceedings of the 16th ACM Symposium on Virtual Reality Software and Technology

November 2009

277 pages

ISBN:9781605588698

DOI:10.1145/1643928

Editor:
Steven N. Spencer
University of Washington
,
General Chairs:
Yoshifumi Kitamura
Osaka University, Japan
,
Haruo Takemura
Osaka University, Japan
,
Program Chairs:
Kiyoshi Kiyokawa
Osaka University, Japan
,
Benjamin Lok
University of Florida
,
Daniel Thalmann
EPFL, Switzerland

Copyright © 2009 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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New York, NY, United States

Publication History

Published: 18 November 2009

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VRST '09

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VRST '09: The 16th ACM Symposium on Virtual Reality Software and Technology

November 18 - 20, 2009

Kyoto, Japan

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Overall Acceptance Rate 66 of 254 submissions, 26%

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Cited By

Tang MHuang TLuo JLi X(2022)PAMS-DP: Building a Unified Open PAMS Human Movement Data Platform2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)10.1109/BIBM55620.2022.9995069(2474-2481)Online publication date: 6-Dec-2022
https://doi.org/10.1109/BIBM55620.2022.9995069
Budikova PSedmidubsky JZezula PCheng WKankanhalli MWang MChu WLiu JWorring M(2021)Efficient Indexing of 3D Human MotionsProceedings of the 2021 International Conference on Multimedia Retrieval10.1145/3460426.3463646(10-18)Online publication date: 24-Aug-2021
https://dl.acm.org/doi/10.1145/3460426.3463646
Sedmidubsky JElias PBudikova PZezula P(2021)Content-Based Management of Human Motion Data: Survey and ChallengesIEEE Access10.1109/ACCESS.2021.30757669(64241-64255)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3075766
Yasin HHussain MWeber A(2020)Keys for Action: An Efficient Keyframe-Based Approach for 3D Action Recognition Using a Deep Neural NetworkSensors10.3390/s2008222620:8(2226)Online publication date: 15-Apr-2020
https://doi.org/10.3390/s20082226
Aberman KWu RLischinski DChen BCohen-Or D(2019)Learning character-agnostic motion for motion retargeting in 2DACM Transactions on Graphics10.1145/3306346.332299938:4(1-14)Online publication date: 12-Jul-2019
https://dl.acm.org/doi/10.1145/3306346.3322999
Men QLeung H(2019)Retrieval of spatial---temporal motion topics from 3D skeleton dataThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-019-01690-x35:6-8(973-984)Online publication date: 1-Jun-2019
https://dl.acm.org/doi/10.1007/s00371-019-01690-x
Kico IGrammalidis NChristidis YLiarokapis F(2018)Digitization and Visualization of Folk Dances in Cultural Heritage: A ReviewInventions10.3390/inventions30400723:4(72)Online publication date: 23-Oct-2018
https://doi.org/10.3390/inventions3040072
Aristidou ACohen-Or DHodgins JChrysanthou YShamir A(2018)Deep motifs and motion signaturesACM Transactions on Graphics10.1145/3272127.327503837:6(1-13)Online publication date: 4-Dec-2018
https://dl.acm.org/doi/10.1145/3272127.3275038
Sedmidubsky JElias PZezula P(2018)Effective and efficient similarity searching in motion capture dataMultimedia Tools and Applications10.1007/s11042-017-4859-777:10(12073-12094)Online publication date: 1-May-2018
https://dl.acm.org/doi/10.1007/s11042-017-4859-7
Li MLeung H(2016)Graph-based representation learning for automatic human motion segmentationMultimedia Tools and Applications10.1007/s11042-016-3480-575:15(9205-9224)Online publication date: 1-Aug-2016
https://dl.acm.org/doi/10.1007/s11042-016-3480-5
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