Abstract
We propose an unsupervised double articulation analyzer for human motion data. Double articulation is a two-layered hierarchical structure underlying in natural language, human motion and other natural data produced by human. A double articulation analyzer estimates the hidden structure of observed data by segmenting and chunking target data. We develop a double articulation analyzer by using a sticky hierarchical Dirichlet process HMM (sticky HDP-HMM), a nonparametric Bayesian model, and an unsupervised morphological analysis based on nested Pitman-Yor language model which can chunk given documents without any dictionaries. We conducted an experiment to evaluate this method. The proposed method could extract unit motions from unsegmented human motion data by analyzing hidden double articulation structure.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
