Abstract
In this study we consider the classification of emblematic gestures based on ensemble methods. In contrast to HMM-based approaches processing a gesture as a whole, we classify trajectory segments comprising a fixed number of sampling points. We propose a multi-view approach in order to increase the diversity of the classifiers across the ensemble by applying different methods for data normalisation and dimensionality reduction and by employing different classifier types. A genetic search algorithm is used to select the most successful ensemble configurations from the large variety of possible combinations. In addition to supervised learning, we make use of both labelled and unlabelled data in an active learning framework in order to reduce the effort required for manual labelling. In the supervised learning scenario, recognition rates per moment in time of more than 86% are obtained, which is comparable to the recognition rates obtained by a HMM approach for complete gestures. The active learning scenario yields recognition rates in excess of 80% even when only a fraction of 20% of all training samples are used.
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Schumacher, J., Sakič, D., Grumpe, A., Fink, G.A., Wöhler, C. (2012). Active Learning of Ensemble Classifiers for Gesture Recognition. In: Pinz, A., Pock, T., Bischof, H., Leberl, F. (eds) Pattern Recognition. DAGM/OAGM 2012. Lecture Notes in Computer Science, vol 7476. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32717-9_50
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DOI: https://doi.org/10.1007/978-3-642-32717-9_50
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