  Although dense local spatial-temporal features with bag-of-features
representation achieve state-of-the-art performance for action recognition, the
huge feature number and feature size prevent current methods from scaling up to
real size problems. In this work, we investigate different types of feature
sampling strategies for action recognition, namely dense sampling, uniformly
random sampling and selective sampling. We propose two effective selective
sampling methods using object proposal techniques. Experiments conducted on a
large video dataset show that we are able to achieve better average recognition
accuracy using 25% less features, through one of proposed selective sampling
methods, and even remain comparable accuracy while discarding 70% features.
