TY - GEN
T1 - Learning spatio-temporal dependencies for action recognition
AU - Cai, Qiao
AU - Yin, Yafeng
AU - Man, Hong
PY - 2013
Y1 - 2013
N2 - In this paper, we propose a spatio-temporal dependencies learning (STDL) method for action recognition. Inspired by self-organizing map, our method can learn implicit spatial-temporal dependencies from sequential action feature sets while preserving the intrinsic topologies characterized in human actions. A further advantage is its ability to project higher dimensional action feature to lower dimensional latent neural distribution, which significantly reduces the computational cost and data redundancy in the learning and recognition process. An ensemble learning strategy using expectation-maximization is adopted to estimate the latent parameters of STDL model. The effectiveness and robustness of the proposed model is verified through extensive experiments on several benchmark datasets.
AB - In this paper, we propose a spatio-temporal dependencies learning (STDL) method for action recognition. Inspired by self-organizing map, our method can learn implicit spatial-temporal dependencies from sequential action feature sets while preserving the intrinsic topologies characterized in human actions. A further advantage is its ability to project higher dimensional action feature to lower dimensional latent neural distribution, which significantly reduces the computational cost and data redundancy in the learning and recognition process. An ensemble learning strategy using expectation-maximization is adopted to estimate the latent parameters of STDL model. The effectiveness and robustness of the proposed model is verified through extensive experiments on several benchmark datasets.
KW - Spatio-temporal dependencies
KW - action recognition
KW - self-organizing map
UR - http://www.scopus.com/inward/record.url?scp=84897750952&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84897750952&partnerID=8YFLogxK
U2 - 10.1109/ICIP.2013.6738771
DO - 10.1109/ICIP.2013.6738771
M3 - Conference contribution
AN - SCOPUS:84897750952
SN - 9781479923410
T3 - 2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings
SP - 3740
EP - 3744
BT - 2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings
T2 - 2013 20th IEEE International Conference on Image Processing, ICIP 2013
Y2 - 15 September 2013 through 18 September 2013
ER -