DocumentCode :
595445
Title :
Action recognition with discriminative mid-level features
Author :
Cuiwei Liu ; Yu Kong ; Xinxiao Wu ; Yunde Jia
Author_Institution :
Beijing Lab. of Intell. Inf. Technol., Beijing Inst. of Technol., Beijing, China
fYear :
2012
fDate :
11-15 Nov. 2012
Firstpage :
3366
Lastpage :
3369
Abstract :
This paper presents a novel random forest learning framework to construct a discriminative and informative mid-level feature from low-level features. Since a single low-level feature based representation is not enough to capture the variations of human appearance, multiple low-level features (i.e., optical flow and histogram of gradient 3D features) are fused to further improve recognition performance. The mid-level feature is employed by a random forest classifier for robust action recognition. Experiments on two publicly available action datasets demonstrate that using both the mid-level feature and the fusion of multiple low-level features leads to a superior performance over previous methods.
Keywords :
feature extraction; image classification; image fusion; image sequences; learning (artificial intelligence); random processes; discriminative midlevel features; histogram-of-gradient 3D features; informative midlevel feature; optical flow; random forest classifier; random forest learning framework; robust action recognition; Feature extraction; Histograms; Humans; Integrated optics; Optical sensors; Training; Vegetation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Pattern Recognition (ICPR), 2012 21st International Conference on
Conference_Location :
Tsukuba
ISSN :
1051-4651
Print_ISBN :
978-1-4673-2216-4
Type :
conf
Filename :
6460886
Link To Document :
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