Abstract
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.
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Denecke, A., Wersing, H., Steil, J.J., Körner, E.: Online figure-ground segmentation with adaptive metrics in generalized LVQ. Neurocomputing 72(7-9), 1470–1482 (2009)
Hamker, F.H.: Life-long learning cell structures —continuously learning without catastrophic interference. Neural Networks 14(4-5), 551–573 (2001)
Kirstein, S., Wersing, H., Körner, E.: A biologically motivated visual memory architecture for online learning of objects. Neural Networks 1, 65–77 (2008)
Fritzke, B.: A growing neural gas network learns topologies. In: Tesauro, G., Touretzky, D.S., Leen, T.K. (eds.) NIPS, pp. 625–632. MIT Press, Cambridge (1994)
Sato, A., Yamada, K.: Generalized learning vector quantization. In: Advances in Neural Information Processing Systems, vol. 7, pp. 423–429 (1995)
Wersing, H., Kirstein, S., Götting, M., Brandl, H., Dunn, M., Mikhailova, I., Goerick, C., Steil, J.J., Ritter, H., Körner, E.: Online learning of objects in a biologically motivated visual architecture. Int. J. Neur. Syst. 17(4), 219–230 (2007)
Schneider, P., Biehl, M., Schleif, F.-M., Hammer, B.: Advanced metric adaptation in Generalized LVQ for classification of mass spectrometry data. In: Proceedings of 6th International Workshop on Self-Organizing Maps (WSOM) (2007)
Crammer, K., Gilad-Bachrach, R., Navot, A., Tishby, N.: Margin analysis of the LVQ algorithm. In: NIPS (2002)
Jirayusakul, A., Auwatanamongkol, S.: A supervised growing neural gas algorithm for cluster analysis. Int. J. Hybrid Intell. Syst. 4(4), 217–229 (2007)
Schleif, F.M., Hammer, B., Villmann, T.: Margin-based active learning for LVQ networks. Neurocomput 70(7-9), 1215–1224 (2007)
Fritzke, B.: The LBG-U method for vector quantization – an improvement over LBG inspired from neural networks. Neural Process. Lett. 5(1), 35–45 (1997)
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© 2009 Springer-Verlag Berlin Heidelberg
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Denecke, A., Wersing, H., Steil, J.J., Körner, E. (2009). Incremental Figure-Ground Segmentation Using Localized Adaptive Metrics in LVQ. In: Príncipe, J.C., Miikkulainen, R. (eds) Advances in Self-Organizing Maps. WSOM 2009. Lecture Notes in Computer Science, vol 5629. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02397-2_6
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DOI: https://doi.org/10.1007/978-3-642-02397-2_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02396-5
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