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Visualising and Clustering Video Data

  • Colin Fyfe
  • Wei Chuang Ooi
  • Hanseok Ko
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4881)

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.

Keywords

Latent Point Video Data English Word Data Space Visual Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Colin Fyfe
    • 1
  • Wei Chuang Ooi
    • 2
  • Hanseok Ko
    • 2
  1. 1.Applied Computational Intelligence Research Unit, The University of PaisleyScotland
  2. 2.Department of Electronics and Computer Engineering, Korea UniversityKorea

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