Learning hierarchical prototypes of motion time series for interactive systems
Großekathöfer, Ulf, Geva, Shlomo, Hermann, Thomas, & Kopp, Stefan (2012) Learning hierarchical prototypes of motion time series for interactive systems. In Proceedings of the ECAI Workshop on Machine Learning for Interactive Systems : Bridging the Gap, Montpelier, France, pp. 37-42.
Abstract. For interactive systems, recognition, reproduction, and generalization of observed motion data are crucial for successful interaction. In this paper, we present a novel method for analysis of motion data that we refer to as K-OMM-trees. K-OMM-trees combine Ordered Means Models (OMMs) a model-based machine learning approach for time series with an hierarchical analysis technique for very large data sets, the K-tree algorithm. The proposed K-OMM-trees enable unsupervised prototype extraction of motion time series data with hierarchical data representation. After introducing the algorithmic details, we apply the proposed method to a gesture data set that includes substantial inter-class variations. Results from our studies show that K-OMM-trees are able to substantially increase the recognition performance and to learn an inherent data hierarchy with meaningful gesture abstractions.
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|Item Type:||Conference Paper|
|Keywords:||Interactive systems, Hierarchical prototypes|
|Subjects:||Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > ELECTRICAL AND ELECTRONIC ENGINEERING (090600)|
|Divisions:||Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
|Copyright Owner:||Copyright 2012 please consult the authors|
|Deposited On:||11 Feb 2013 00:20|
|Last Modified:||12 Jun 2013 15:30|
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