Visualising and clustering video data

Authors:
Colin Fyfe;Wei Chuang Ooi;Hanseok Ko
Affiliations:
Applied Computational Intelligence Research Unit, The University of Paisley, Scotland;Department of Electronics and Computer Engineering, Korea University, Korea;Department of Electronics and Computer Engineering, Korea University, Korea
Venue:
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Year:
2007

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Abstract

We review a new form of self-organizing map which is based on a nonlinear projection of latent points into data space, identical to that performed in the Generative Topographic Mapping (GTM) [1]. But whereas the GTM is an extension of a mixture of experts, this model is an extension of a product of experts [6]. We show visualisation and clustering results on a data set composed of video data of lips uttering 5 Korean vowels and show that the new mapping achieves better results than the standard Self-Organizing Map.