Neural competition for motion segmentation

We present a system for sensory classification and segmenta- tion of motion trajectories. It consists of a combination of manifolds from Unsupervised Kernel Regression (UKR) and the recurrent neural Competi- tive Layer Model (CLM). The UKR manifolds hold learned representations of a set of candidate motions and the CLM dynamics, working on features defined in the UKR domain, realises the segmentation of observed tra- jectory data according to the competing candidates. The evaluation on trajectories describing four different letters yields improved classification results compared to our previous, pure manifold approach.