Growing multi-dimensional self-organizing maps for motion detection

Authors:
Udo Seiffert
Affiliations:
Univ. of Magdeburg, Germany
Venue:
Self-Organizing neural networks
Year:
2001

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Abstract

The standard Self-Organizing Map consists of a two-dimensional rectangular grid of neurons. For many applications this represents a very good target to reduce the dimensionality of the input data. However, occasionally a multi-dimensional layer, keeping more than two dimensions of teh input data, might be more advantageous. This sometimes also called hypercube topoloy can be considered as the universal case of the standard topoology. This chapter gives an introduction and demonstrates basic properties by means of applications from motion picture analysis.