TY - JOUR
T1 - Human activity recognition using robust adaptive privileged probabilistic learning
AU - Vrigkas, Michalis
AU - Kazakos, Evangelos
AU - Nikou, Christophoros
AU - Kakadiaris, Ioannis A.
N1 - Publisher Copyright:
© 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd. part of Springer Nature.
PY - 2021/8
Y1 - 2021/8
N2 - In this work, a supervised probabilistic approach is proposed that integrates the learning using privileged information (LUPI) paradigm into a hidden conditional random field (HCRF) model, called HCRF+, for human action recognition. The proposed model employs a self-training technique for automatic estimation of the regularization parameters of the objective function. Moreover, the method provides robustness to outliers by modeling the conditional distribution of the privileged information by a Student’s t-density function, which is naturally integrated into the HCRF+ framework. The proposed method was evaluated using different forms of privileged information on four publicly available datasets. The experimental results demonstrate its effectiveness concerning the state of the art in the LUPI framework using both hand-crafted and deep learning-based features extracted from a convolutional neural network.
AB - In this work, a supervised probabilistic approach is proposed that integrates the learning using privileged information (LUPI) paradigm into a hidden conditional random field (HCRF) model, called HCRF+, for human action recognition. The proposed model employs a self-training technique for automatic estimation of the regularization parameters of the objective function. Moreover, the method provides robustness to outliers by modeling the conditional distribution of the privileged information by a Student’s t-density function, which is naturally integrated into the HCRF+ framework. The proposed method was evaluated using different forms of privileged information on four publicly available datasets. The experimental results demonstrate its effectiveness concerning the state of the art in the LUPI framework using both hand-crafted and deep learning-based features extracted from a convolutional neural network.
KW - Hidden conditional random fields
KW - Human activity recognition
KW - Learning using privileged information
KW - Student’s t-distribution
UR - http://www.scopus.com/inward/record.url?scp=85098789566&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85098789566&partnerID=8YFLogxK
U2 - 10.1007/s10044-020-00953-x
DO - 10.1007/s10044-020-00953-x
M3 - Article
AN - SCOPUS:85098789566
SN - 1433-7541
VL - 24
SP - 915
EP - 932
JO - Pattern Analysis and Applications
JF - Pattern Analysis and Applications
IS - 3
ER -