Determining the scale of interest regions in videos

A number of action recognition methods make use of spatio-temporal features. These features often consist of local spatio-temporal descriptors centered at locations provided by an interest point detector. The extracted descriptors will then serve as input to classification algorithms. The correct scale of these descriptors is an essential parameter to be determined. Improved information quality has been achieved from recently developed entropy-based spatio-temporal feature descriptors. In this paper, we present an approach for determining scales of the sub-volumes of interest given the locations of spatio-temporal features. Our method works by measuring the average variations of local motion content calculated on subsequences of motion filter responses. We design a filter-specific data prior that allows to determine the scales of the informative neighborhoods. We demonstrate that features calculated at the scales provided by or method allow for noticeable performance improvements of action recognition algorithms.