A comparison of unsupervised learning algorithms for gesture clustering
- Resource Type
- Conference
- Authors
- Ball, Adrian; Rye, David; Ramos, Fabio; Velonaki, Mari
- Source
- 2011 6th ACM/IEEE International Conference on Human-Robot Interaction (HRI) Human-Robot Interaction (HRI), 2011 6th ACM/IEEE International Conference on. :111-112 Mar, 2011
- Subject
- Robotics and Control Systems
Signal Processing and Analysis
Computing and Processing
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Clustering algorithms
Unsupervised learning
Measurement
Clustering methods
Algorithm design and analysis
Robots
Educational institutions
Gesture recognition
unsupervised clustering
v-measure
- Language
- ISSN
- 2167-2121
2167-2148
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.