A comparison of unsupervised learning algorithms for gesture clustering

  • Ball A
  • Rye D
  • Ramos F
 et al. 
  • 36

    Readers

    Mendeley users who have this article in their library.
  • 2

    Citations

    Citations of this article.

Abstract

Gesture recognition is an important aspect of interpersonal social interaction. Developing a similar capacity in a robot will improve human-robot interaction. Various unsupervised clustering methods applied to clustering a set of dynamic human arm gestures are compared. Unsupervised cluster- ing is important in gesture recognition as it imposes no a priori bound on the set of gestures. Results are compared using v-measure, a metric that allows differential weighting between clustering homogeneity and completeness. Experi- ments show that the best clustering method depends on the desired balance between homogeneity and completeness.

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in

Find this document

Authors

  • Adrian Ball

  • David Rye

  • Fabio Ramos

  • Mari Velonaki

Cite this document

Choose a citation style from the tabs below

Save time finding and organizing research with Mendeley

Sign up for free