Action classification using data mining and Paris of SURF-based trajectories

A new action classification approach is proposed to improve the accuracy of the state-of-art frameworks from three folds: (1) Association rule mining is used with dense trajectories approach to discover strong relations between different visual words in the video clips, then a new histogram is built for each video clip based on such relations. (2) The second proposed approach is based on SURF descriptor to extract the most similar pairs of dense trajectories' features, and then the most similar trajectories' features are used to describe the video clip. (3) Finally, a symmetrical SURFs approach is used to detect the symmetrical pairs of trajectories in the video; the most symmetrical features in the video clip are extracted and used to describe the video clip. The above three new features are used in addition to the original dense trajectories' features for action classification. The importance of these new features is that many features are not related to the background and can significantly increase the overall recognition accuracy.