LEARNING ZEROTH CLASS DICTIONARY FOR HUMAN ACTION RECOGNITION

JIAXIN CAI, lifang zhang

Abstract:

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.