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.

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Abstract

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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ID Code: 57108
Item Type: Conference Paper
Refereed: Yes
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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