Title | Model probability in self-organising maps |
---|
Authors | Angelopoulou, A., Psarrou, A., García-Rodríguez, J., Mentzelopoulos, M. and Gupta, G. |
---|
Type | Conference paper |
---|
Abstract | 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. |
---|
Keywords | Minimum Description Length, Self-organising networks, Shape Modelling |
---|
Year | 2013 |
---|
Conference | 12th International Work-Conference on Artificial Neural Networks, IWANN 2013 |
---|
Publisher | Springer |
---|
Publication dates |
---|
Published | 04 Sep 2013 |
---|
Book title | Advances in Computational Intelligence: 12th International Work-Conference on Artificial Neural Networks, IWANN 2013 |
---|
ISBN | 9783642386817 |
---|
Digital Object Identifier (DOI) | https://doi.org/10.1007/978-3-642-38682-4_1 |
---|