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Following non-stationary distributions by controlling the vector quantization accuracy of a growing neural gas network

Abstract : In this paper, an original method extended from growing neural gas (GNG-T) [B. Fritzke, A growing neural gas network learns topologies, in: G. Tesauro, D.S. Touretzky, T.K. Leen (Eds.), Advances in Neural Information Processing Systems 7, MIT Press, Cambridge, MA, 1995, pp. 625–632] is presented. The method performs continuously vector quantization over a distribution that changes over time. It deals with both sudden changes and continuous ones, and is thus suited for the video tracking framework, where continuous tracking is required as well as fast adaptation to incoming and outgoing people. The central mechanism relies on the management of the quantization resolution, that copes with stopping condition problems of usual GNG inspired methods. Application to video tracking is presented.
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https://hal-supelec.archives-ouvertes.fr/hal-00282087
Contributor : Sébastien van Luchene <>
Submitted on : Monday, May 26, 2008 - 1:15:53 PM
Last modification on : Friday, April 12, 2019 - 3:58:23 PM

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Hervé Frezza-Buet. Following non-stationary distributions by controlling the vector quantization accuracy of a growing neural gas network. Neurocomputing, Elsevier, 2008, 71 (7-9), pp.1191-1202. ⟨10.1016/j.neucom.2007.12.024⟩. ⟨hal-00282087⟩

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