Title | Adaptive learning in motion analysis with self-organising maps |
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Authors | Angelopoulou, A., Garcia-Rodriguez, J., Psarrou, A., Gupta, G. and Mentzelopoulos, M. |
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Type | Conference paper |
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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. This model is used to the representation of motion in image sequences by initialising a suitable segmentation. We present experimental results for hands and we quantitatively evaluate the matching capabilities of the proposed method with the topographic product. |
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Keywords | Image Sequences |
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| Pattern Clustering |
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| Self-organising feature maps |
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| Topology |
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Year | 2013 |
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Conference | International Joint Conference on Neural Networks (IJCNN) |
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Publisher | IEEE |
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Publication dates |
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Published | Aug 2013 |
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Journal citation | pp. 1-7 |
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ISSN | 2161-4393 |
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Book title | The 2013 International Joint Conference on Neural Networks (IJCNN) |
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ISBN | 978-1-4673-6128-6 |
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Digital Object Identifier (DOI) | https://doi.org/10.1109/IJCNN.2013.6707135 |
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