  We address the problem of generating video features for action recognition.
The spatial pyramid and its variants have been very popular feature models due
to their success in balancing spatial location encoding and spatial invariance.
Although it seems straightforward to extend spatial pyramid to the temporal
domain (spatio-temporal pyramid), the large spatio-temporal diversity of
unconstrained videos and the resulting significantly higher dimensional
representations make it less appealing. This paper introduces the space-time
extended descriptor, a simple but efficient alternative way to include the
spatio-temporal location into the video features. Instead of only coding motion
information and leaving the spatio-temporal location to be represented at the
pooling stage, location information is used as part of the encoding step. This
method is a much more effective and efficient location encoding method as
compared to the fixed grid model because it avoids the danger of over
committing to artificial boundaries and its dimension is relatively low.
Experimental results on several benchmark datasets show that, despite its
simplicity, this method achieves comparable or better results than
spatio-temporal pyramid.
