Motion prediction using VC-generalization bounds

Harry Wechsler, Zoran Duric, Li Fayin, Vladimir S. Cherkassky

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)151-154
Number of pages4
JournalProceedings - International Conference on Pattern Recognition
Issue number1
StatePublished - Dec 1 2002


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