  In this paper, a discriminative two-phase dictionary learning framework is
proposed for classifying human action by sparse shape representations, in which
the first-phase dictionary is learned on the selected discriminative frames and
the second-phase dictionary is built for recognition using reconstruction
errors of the first-phase dictionary as input features. We propose a "zeroth
class" trick for detecting undiscriminating frames of the test video and
eliminating them before voting on the action categories. Experimental results
on benchmarks demonstrate the effectiveness of our method.
