Growing models have been widely used for clustering or topology learning. Traditionally these models work on stationary environments, grow incrementally and adapt their nodes to a given distribution based on global parameters. In this paper, we present an enhanced unsupervised self-organising network for the modelling of visual objects. We first develop a framework for building non-rigid shapes using the growth mechanism of the self-organising maps, and then we define an optimal number of nodes without overfitting or underfitting the network based on the knowledge obtained from information-theoretic considerations. We present experimental results for hands and we quantitatively evaluate the matching capabilities of the proposed method with the topographic product. © 2013 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Angelopoulou, A., Psarrou, A., García-Rodríguez, J., Mentzelopoulos, M., & Gupta, G. (2013). Model probability in self-organising maps. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7903 LNCS, pp. 1–10). https://doi.org/10.1007/978-3-642-38682-4_1
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