research-article

Scalable Organization of Collections of Motion Capture Data via Quantitative and Qualitative Analysis

Authors:

Yan ZhangAuthors Info & Claims

ICMR '15: Proceedings of the 5th ACM on International Conference on Multimedia Retrieval

Pages 411 - 418

https://doi.org/10.1145/2671188.2749384

Published: 22 June 2015 Publication History

Abstract

This paper proposes a scalable method for organizing the collection of motion capture data for overview and exploration, and it mainly addresses three core problems, including data abstraction, neighborhood construction and data visualization. To alleviate the contradiction between limited visual space and the ever-increasing size of real-word datasets, hierarchical affinity propagation (HAP) is adopt to perform data abstraction on low-level pose features to generate multi-layers of data aggregations in consistent with coarse to fine abstraction levels of human cognition. To construct a meaningful neighborhood for user choosing a browsing path and positioning themselves, quartet analysis-based phylogenetic tree is created upon high-level pose features to produce more reliable neighbors for different aggregations of the specific abstraction level. To provide a convenient interactive environment for user navigation, a phylogenetic tree-centric visualization strategy in three-dimensional space is present. Experimental results on HDM05 motion capture dataset verify the effectiveness of the proposed method.

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Cited By

Chen SZhao XLuo BSun Z(2020)Visual Browse and Exploration in Motion Capture Data with Phylogenetic Tree of Context-Aware PosesSensors10.3390/s2018522420:18(5224)Online publication date: 13-Sep-2020
https://doi.org/10.3390/s20185224
Gan YZhang YSun ZZhang HTagliasacchi ATeather R(2019)Qualitative Organization of Photo Collections via Quartet Analysis and Active LearningProceedings of the 45th Graphics Interface Conference10.20380/GI2019.06(1-8)Online publication date: 1-Jun-2019
https://dl.acm.org/doi/10.20380/GI2019.06
Aristidou AShamir AChrysanthou Y(2019)Digital Dance EthnographyJournal on Computing and Cultural Heritage 10.1145/334438312:4(1-27)Online publication date: 17-Nov-2019
https://dl.acm.org/doi/10.1145/3344383
Show More Cited By

Index Terms

Scalable Organization of Collections of Motion Capture Data via Quantitative and Qualitative Analysis
1. Information systems
  1. Information retrieval
    1. Document representation
2. Theory of computation
  1. Semantics and reasoning
    1. Program reasoning
      1. Abstraction

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Published In

cover image ACM Conferences

ICMR '15: Proceedings of the 5th ACM on International Conference on Multimedia Retrieval

June 2015

700 pages

ISBN:9781450332743

DOI:10.1145/2671188

General Chairs:
Alex Hauptmann
Carnegie Mellon University, USA
,
Chong-Wah Ngo
City University of Hong Kong, China
,
Xiangyang Xue
Fudan University, China
,
Program Chairs:
Yu-Gang Jiang
Fudan University, China
,
Cees Snoek
University of Amsterdam and Qualcomm Research Netherlands
,
Nuno Vasconcelos
University of California, San Diego, USA

Copyright © 2015 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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SIGMM: ACM Special Interest Group on Multimedia

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 June 2015

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Funding Sources

Science and Technology Plan of Jiangsu Province
Innovation Foundation of State Key Lab for Novel Software Technology of China
Program for New Century Excellent Talents of Ministry of Education of China
National Natural Science Foundation of China

Conference

ICMR '15

Sponsor:

SIGMM

ICMR '15: International Conference on Multimedia Retrieval

June 23 - 26, 2015

Shanghai, China

Acceptance Rates

ICMR '15 Paper Acceptance Rate 48 of 127 submissions, 38%;

Overall Acceptance Rate 254 of 830 submissions, 31%

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Cited By

Chen SZhao XLuo BSun Z(2020)Visual Browse and Exploration in Motion Capture Data with Phylogenetic Tree of Context-Aware PosesSensors10.3390/s2018522420:18(5224)Online publication date: 13-Sep-2020
https://doi.org/10.3390/s20185224
Gan YZhang YSun ZZhang HTagliasacchi ATeather R(2019)Qualitative Organization of Photo Collections via Quartet Analysis and Active LearningProceedings of the 45th Graphics Interface Conference10.20380/GI2019.06(1-8)Online publication date: 1-Jun-2019
https://dl.acm.org/doi/10.20380/GI2019.06
Aristidou AShamir AChrysanthou Y(2019)Digital Dance EthnographyJournal on Computing and Cultural Heritage 10.1145/334438312:4(1-27)Online publication date: 17-Nov-2019
https://dl.acm.org/doi/10.1145/3344383
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
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
Voulodimos ARallis IDoulamis N(2018)Physics-based keyframe selection for human motion summarizationMultimedia Tools and Applications10.1007/s11042-018-6935-z79:5-6(3243-3259)Online publication date: 7-Dec-2018
https://doi.org/10.1007/s11042-018-6935-z

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