Adaptive learning in motion analysis with self-organising maps

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.