Motion prediction using VC-generalization bounds

This paper describes a novel application of Statistical Learning Theory (SLT) for motion prediction. SLT provides analytical VC-generalization bounds for model selection; these bounds relate unknown prediction risk (generalization performance) and known quantities such as the number of training samples, empirical error, and a measure of model complexity called the VC-dimension. We use the VC-generalization bounds for the problem of choosing optimal motion models from small sets of image measurements (flow). We present results of experiments on image sequences for motion interpolation and extrapolation; these results demonstrate the strengths of our approach.