  Many efforts have been devoted to develop alternative methods to traditional
vector quantization in image domain such as sparse coding and soft-assignment.
These approaches can be split into a dictionary learning phase and a feature
encoding phase which are often closely connected. In this paper, we investigate
the effects of these phases by separating them for video-based action
classification. We compare several dictionary learning methods and feature
encoding schemes through extensive experiments on KTH and HMDB51 datasets.
Experimental results indicate that sparse coding performs consistently better
than the other encoding methods in large complex dataset (i.e., HMDB51), and it
is robust to different dictionaries. For small simple dataset (i.e., KTH) with
less variation, however, all the encoding strategies perform competitively. In
addition, we note that the strength of sophisticated encoding approaches comes
not from their corresponding dictionaries but the encoding mechanisms, and we
can just use randomly selected exemplars as dictionaries for video-based action
classification.
