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Authors: Husam Al-Behadili 1 ; Arne Grumpe 2 and Christian Wöhler 2

Affiliations: 1 University of Mustansiriyah and TU Dortmund University, Iraq ; 2 TU Dortmund University, Germany

ISBN: 978-989-758-175-5

Keyword(s): Data Stream, Nearest Class Mean, Incremental Learning, Semi-supervised Learning, Kernel.

Related Ontology Subjects/Areas/Topics: Applications and Services ; Computer Vision, Visualization and Computer Graphics ; Enterprise Information Systems ; Features Extraction ; Human and Computer Interaction ; Human-Computer Interaction ; Image and Video Analysis

Abstract: The automatic recognition of gestures is important in a variety of applications, e.g. human-machine-interaction. Commonly, different individuals execute gestures in a slightly different manner and thus a fully labelled dataset is not available while unlabelled data may be acquired from an on-line stream. Consequently, gesture recognition systems should be able to be trained in a semi-supervised learning scenario. Additionally, real-time systems and large-scale data require a dimensionality reduction of the data to reduce the processing time. This is commonly achieved by linear subspace projections. Most of the gesture data sets, however, are non-linearly distributed. Hence, linear sub-space projection fails to separate the classes. We propose an extension to linear subspace projection by applying a non-linear transformation to a space of higher dimensional after the linear subspace projection. This mapping, however, is not explicitly evaluated but implicitly used by a kernel function. The kernel nearest class mean (KNCM) classifier is shown to handle the non-linearity as well as the semi-supervised learning scenario. The computational expense of the non-linear kernel function is compensated by the dimensionality reduction of the previous linear subspace projection. The method is applied to a gesture dataset comprised of 3D trajectories. The trajectories were acquired using the Kinect sensor. The results of the semi-supervised learning show high accuracies that approach the accuracy of a fully supervised scenario already for small dimensions of the subspace and small training sets. The accuracy of the semi-supervised KNCM exceeds the accuracy of the original nearest class mean classifier in all cases. (More)

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Paper citation in several formats:
Al-Behadili, H.; Grumpe, A. and Wöhler, C. (2016). Non-linear Distance-based Semi-supervised Multi-class Gesture Recognition.In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016) ISBN 978-989-758-175-5, pages 280-286. DOI: 10.5220/0005674102800286

@conference{visapp16,
author={Husam Al{-}Behadili. and Arne Grumpe. and Christian Wöhler.},
title={Non-linear Distance-based Semi-supervised Multi-class Gesture Recognition},
booktitle={Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016)},
year={2016},
pages={280-286},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005674102800286},
isbn={978-989-758-175-5},
}

TY - CONF

JO - Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016)
TI - Non-linear Distance-based Semi-supervised Multi-class Gesture Recognition
SN - 978-989-758-175-5
AU - Al-Behadili, H.
AU - Grumpe, A.
AU - Wöhler, C.
PY - 2016
SP - 280
EP - 286
DO - 10.5220/0005674102800286

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