High order co-occurrence of visualwords for action recognition

This paper exploits the high order co-occurrence information for human action representation. Based on the bag-of-words (BoW) model, visual words are mapped into a co-occurrence space through latent semantic analysis (LSA). High order co-occurrence of the visual words is well captured and therefore the representation of actions in the co-occurrence space becomes more informative and compact. Since the representation is effective and efficient, and is less affected by the sizes of the codebook, it can be easily integrated into models based on BoW. Evaluations on the benchmark KTH dataset and the realistic HMDB51 dataset demonstrates that the proposed approach significantly improves the baseline BoW model and therefore is promising for human action recognition.