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A comparison of unsupervised learning algorithms for gesture clustering

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4 Author(s)
Ball, A. ; Centre for Social Robot., Univ. of Sydney, Sydney, NSW, Australia ; Rye, D. ; Ramos, F. ; Velonaki, M.

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 clustering 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. Experiments show that the best clustering method depends on the desired balance between homogeneity and completeness.

Published in:

Human-Robot Interaction (HRI), 2011 6th ACM/IEEE International Conference on

Date of Conference:

8-11 March 2011

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