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2009
Springer

Incremental Figure-Ground Segmentation Using Localized Adaptive Metrics in LVQ

10 years 1 months ago
Incremental Figure-Ground Segmentation Using Localized Adaptive Metrics in LVQ
Vector quantization methods are confronted with a model selection problem, namely the number of prototypical feature representatives to model each class. In this paper we present an incremental learning scheme in the context of figure-ground segmentation. In presence of local adaptive metrics and supervised noisy information we use a parallel evaluation scheme combined with a local utility function to organize a learning vector quantization (LVQ) network with an adaptive number of prototypes and verify the capabilities on a real world figure-ground segmentation task.
Alexander Denecke, Heiko Wersing, Jochen J. Steil,
Added 25 May 2010
Updated 25 May 2010
Type Conference
Year 2009
Where WSOM
Authors Alexander Denecke, Heiko Wersing, Jochen J. Steil, Edgar Körner
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