DocumentCode :
2710288
Title :
A Joint Matrix Factorization Approach to Unsupervised Action Categorization
Author :
Cui, Peng ; Wang, Fei ; Sun, Li-Feng ; Yang, Shi-Qiang
Author_Institution :
Comput. Sci. Dept., Tsinghua Univ., Beijing
fYear :
2008
fDate :
15-19 Dec. 2008
Firstpage :
767
Lastpage :
772
Abstract :
In this paper, a novel unsupervised approach to mining categories from action video sequences is presented. This approach consists of two modules: action representation and learning model. Videos are regarded as spatially distributed dynamic pixel time series, which are quantized into pixel prototypes. After replacing the pixel time series with their corresponding prototype labels, the video sequences are compressed into 2D action matrices. We put these matrices together to form an multi-action tensor, and propose the joint matrix factorization method to simultaneously cluster the pixel prototypes into pixel signatures, and matrices into action classes. The approach is tested on public and popular Weizmann data set, and promising results are achieved.
Keywords :
image sequences; matrix decomposition; video signal processing; 2D action matrices; Weizmann data set; action representation; action video sequences; learning model; matrix factorization; unsupervised action categorization; Computer science; Electroencephalography; Feature extraction; Matrix converters; Matrix decomposition; Prototypes; Surveillance; Symmetric matrices; Tensile stress; Video sequences; Action categorization; Joint matrix factorization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Data Mining, 2008. ICDM '08. Eighth IEEE International Conference on
Conference_Location :
Pisa
ISSN :
1550-4786
Print_ISBN :
978-0-7695-3502-9
Type :
conf
DOI :
10.1109/ICDM.2008.59
Filename :
4781176
Link To Document :
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