High order co-occurrence of visual words for action recognition

Zhang, Lei, Zhen, Xiantong and Shao, Ling (2012) High order co-occurrence of visual words for action recognition. In: ICIP 2012 - 19th IEEE International Conference on Image Processing, 30th September - 3rd October 2012, Orlando, Florida.

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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.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Action Recognition, High Order Co-occurrence, Latent Semantic Analysis
Subjects:	G400 Computer Science
Department:	Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User:	Paul Burns
Date Deposited:	16 Jun 2015 14:31
Last Modified:	13 Oct 2019 00:32
URI:	http://nrl.northumbria.ac.uk/id/eprint/22962

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