Efficient distance-based gestural pattern mining in spatiotemporal 3D motion capture databases

Christian Beecks, M. Hassani, F. Obeloer, Thomas Seidl

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

3 Citations (Scopus)

Abstract

One of the most fundamental challenges when mining gestural patterns in 3D motion capture databases is the definition of spatiotemporal similarity between two gestural patterns. While time-elastic similarity models such as the Gesture Matching Distance on gesture signatures are able to leverage the spatial and temporal characteristics of gestural patterns, the applicability of such distance-based models in order to analyze large 3D motion capture databases is limited due to their high computational complexity. To this end, we propose a lower bound approximation of the Gesture Matching Distance that preserves the spatiotemporal characteristics and can be utilized in an optimal multi-step k-nearest-neighbor search architecture in order to analyze and mine spatiotemporal databases efficiently. We empirically investigate the performance in terms of accuracy and efficiency based on 3D motion capture databases and show that our lower bound approximation is able to achieve an increase in efficiency of more than one order of magnitude with a negligible loss in accuracy. Our proposal is fundamental for efficient distance-based gestural pattern mining.
Original languageEnglish
Title of host publication15th IEEE International Conference on Data Mining workshop 14-17 November 2015, Atlantic City, New Jersey : Proceedings
EditorsP. Cui, J. Dy, C. Aggarwal, Z.-H. Zhou, A. Tuzhilin, H. Xiong, X. Wu
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Pages1425-1432
Number of pages8
ISBN (Electronic)978-1-4673-8493-3
ISBN (Print)9781467384926
DOIs
Publication statusPublished - 29 Jan 2016
Externally publishedYes
Event15th IEEE International Conference on Data Mining (ICDM 2015) - Atlantic City, United States
Duration: 14 Nov 201517 Nov 2015
Conference number: 15
https://icdm2015.stonybrook.edu/

Conference

Conference15th IEEE International Conference on Data Mining (ICDM 2015)
Abbreviated titleICDM 2015
CountryUnited States
CityAtlantic City
Period14/11/1517/11/15
Internet address

Keywords

  • 3D motion capture data
  • Gesture matching distance
  • gestural pattern mining
  • gesture signature
  • spatiotemporal similarity search

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