High order co-occurrence of visual words for action recognition

Lei Zhang, Xiantong Zhen, Ling Shao

Research output: Contribution to conferencePaper

3 Citations (Scopus)

Abstract

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.
Original languageEnglish
DOIs
Publication statusPublished - Oct 2012
EventICIP 2012 - 19th IEEE International Conference on Image Processing - Orlando, Florida
Duration: 1 Oct 2012 → …

Conference

ConferenceICIP 2012 - 19th IEEE International Conference on Image Processing
Period1/10/12 → …

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