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
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)