Abstract
Self-organising neural networks try to preserve the topology of an input space by using their competitive learning. This capacity has been used, among others, for the representation of objects and their motion. In this work we use a kind of self-organising network, the Growing Neural Gas, to represent objects as a result of an adaptive process by a topology-preserving graph that constitutes an induced Delaunay triangulation of their shapes. In this paper we present a new hybrid architecture that creates multiple specialized maps to represent different clusters obtained from the multilevel multispectral threshold segmentation.
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García-Rodríguez, J., Flórez-Revuelta, F., García-Chamizo, J.M. (2008). Hybrid GNG Architecture Learns Features in Images. In: Corchado, E., Abraham, A., Pedrycz, W. (eds) Hybrid Artificial Intelligence Systems. HAIS 2008. Lecture Notes in Computer Science(), vol 5271. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87656-4_56
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DOI: https://doi.org/10.1007/978-3-540-87656-4_56
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-87655-7
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