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An action consists of a sequence of instantaneous motion patterns whose temporal ordering contains critical information especially for distinguishing fine-grained action categories. However, existing action recognition methods are dominated by discriminative classifiers such as kernel machines or metric learning with Bag-of-Words (BoW) action representations. They ignore the temporal structures of actions in exchange for robustness against noise. Although such temporal structures can bedoi:10.1109/iwssip.2015.7313922 dblp:conf/iwssip/AlexiouXG15 fatcat:a6d3wlqaejcsxf3cvlvomf5hmq