TY - JOUR
T1 - Action categorization with modified hidden conditional random field
AU - Zhang, Jianguo
AU - Gong, Shaogang
PY - 2010
Y1 - 2010
N2 - In this paper, we present a method for action categorization with a modified hidden conditional random field (HCRF). Specifically, effective silhouette-based action features are extracted using motion moments and spectrum of chain code. We formulate a modified HCRF (mHCRF) to have a guaranteed global optimum in the modelling of the temporal action dependencies after the HMM pathing stage. Experimental results on action categorization using this model are compared favorably against several existing model-based methods including GMM, SVM, Logistic Regression, HMM, CRF and HCRF.
AB - In this paper, we present a method for action categorization with a modified hidden conditional random field (HCRF). Specifically, effective silhouette-based action features are extracted using motion moments and spectrum of chain code. We formulate a modified HCRF (mHCRF) to have a guaranteed global optimum in the modelling of the temporal action dependencies after the HMM pathing stage. Experimental results on action categorization using this model are compared favorably against several existing model-based methods including GMM, SVM, Logistic Regression, HMM, CRF and HCRF.
UR - http://www.scopus.com/inward/record.url?scp=68949131112&partnerID=8YFLogxK
U2 - 10.1016/j.patcog.2009.05.015
DO - 10.1016/j.patcog.2009.05.015
M3 - Article
AN - SCOPUS:68949131112
SN - 0031-3203
VL - 43
SP - 197
EP - 203
JO - Pattern Recognition
JF - Pattern Recognition
IS - 1
ER -