  We propose a method for representing motion information for video
classification and retrieval. We improve upon local descriptor based methods
that have been among the most popular and successful models for representing
videos. The desired local descriptors need to satisfy two requirements: 1) to
be representative, 2) to be discriminative. Therefore, they need to occur
frequently enough in the videos and to be be able to tell the difference among
different types of motions. To generate such local descriptors, the video
blocks they are based on must contain just the right amount of motion
information. However, current state-of-the-art local descriptor methods use
video blocks with a single fixed size, which is insufficient for covering
actions with varying speeds. In this paper, we introduce a long-short term
motion feature that generates descriptors from video blocks with multiple
lengths, thus covering motions with large speed variance. Experimental results
show that, albeit simple, our model achieves state-of-the-arts results on
several benchmark datasets.
