Global Coordination Based on Matrix Neural Gas for Dynamic Texture Synthesis

  • Banchar Arnonkijpanich
  • Barbara Hammer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5998)


Matrix neural gas has been proposed as a mathematically well-founded extension of neural gas networks to represent data in terms of prototypes and local principal components in a smooth way. The additional information provided by local principal directions can directly be combined with charting techniques such that a nonlinear embedding of a data manifold into low dimensions results for which an explicit function as well as an approximate inverse exists. In this paper, we show that these ingredients can be used to embed dynamic textures in low dimensional spaces such that, together with a traversing technique in the low dimensional representation, efficient dynamic texture synthesis can be obtained.


Texture Synthesis Dynamic Texture Approximate Inverse Data Manifold Matrix Learning 
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 2010

Authors and Affiliations

  • Banchar Arnonkijpanich
    • 1
    • 2
  • Barbara Hammer
    • 3
  1. 1.Department of Mathematics, Faculty of ScienceKhon Kaen UniversityThailand
  2. 2.Centre of Excellence in MathematicsThe Commission on Higher EducationBangkokThailand
  3. 3.CITECUniversity of BielefeldBielefeldGermany

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