Merging linear discriminant analysis with Bag of Words model for human action recognition

In this paper we propose a novel method for human action recognition, that unifies discriminative Bag of Words (BoW)-based video representation and discriminant subspace learning. An iterative optimization scheme is proposed for sequential discriminant BoWs-based action representation and code-book adaptation based on action discrimination in a reduced dimensionality feature space where action classes are better discriminated. Experiments on four publicly available action recognition data sets demonstrate that the proposed unified approach increases the discriminative ability of the obtained video representation, providing enhanced action classification performance.