Action categorization with modified hidden conditional random field

被引：51
作者：
Zhang, Jianguo [1 ]
Gong, Shaogang [2 ]
机构：
[1] Queens Univ Belfast, Sch Elect Elect Engn & Comp Sci, Belfast BT7 1NN, Antrim, North Ireland
[2] Queen Mary Univ London, Dept Comp Sci, London E1 4NS, England
来源：
PATTERN RECOGNITION | 2010年 / 43卷 / 01期
基金：
英国工程与自然科学研究理事会;
关键词：
Action recognition; Graph model; Hidden conditional random field; Optimum learning; RECOGNITION;
D O I：
10.1016/j.patcog.2009.05.015
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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. (C) 2009 Elsevier Ltd. All rights reserved.
引用
收藏
页码：197 / 203
页数：7
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