Partial Least Squares kernel for computing similarities between video sequences

Computing similarities between data samples is a fundamental step in most Pattern Recognition (PR) tasks. Better similarity measures lead to more accurate prediction of labels. Computing similarities between video sequences has been a challenging problem for the PR community for long because videos have both spatial and temporal context which are hard to capture. We describe a novel approach that employs Partial Least Squares (PLS) regression to derive a measure of similarity between two tensors (videos). We demonstrate the use of this tensor similarity measure along with SVM classifiers to solve the tasks of hand gesture recognition and action classification. We show that our methods significantly outperform the state of the art approaches on two popular datasets: Cambridge hand gesture dataset and UCF sports action dataset. Our method requires no parameter tuning.