TY - GEN
T1 - Inferring human activities using robust privileged probabilistic learning
AU - Vrigkas, Michalis
AU - Kazakos, Evangelos
AU - Nikou, Christophoros
AU - Kakadiaris, Ioannis A.
N1 - Funding Information:
This work was funded in part by the UH Hugh Roy and Lillie Cranz Cullen Endowment Fund and by the European Commission (H2020-MSCA-IF-2014), under grant agreement No. 656094. All statements of fact, opinion or conclusions contained herein are those of the authors and should not be construed as representing the official views or policies of the sponsors.
Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/1
Y1 - 2017/7/1
N2 - Classification models may often suffer from 'structure imbalance' between training and testing data that may occur due to the deficient data collection process. This imbalance can be represented by the learning using privileged information (LUPI) paradigm. In this paper, we present a supervised probabilistic classification approach that integrates LUPI into a hidden conditional random field (HCRF) model. The proposed model is called LUPI-HCRF and is able to cope with additional information that is only available during training. Moreover, the proposed method employes Student's t-distribution to provide robustness to outliers by modeling the conditional distribution of the privileged information. Experimental results in three publicly available datasets demonstrate the effectiveness of the proposed approach and improve the state-of-The-Art in the LUPI framework for recognizing human activities.
AB - Classification models may often suffer from 'structure imbalance' between training and testing data that may occur due to the deficient data collection process. This imbalance can be represented by the learning using privileged information (LUPI) paradigm. In this paper, we present a supervised probabilistic classification approach that integrates LUPI into a hidden conditional random field (HCRF) model. The proposed model is called LUPI-HCRF and is able to cope with additional information that is only available during training. Moreover, the proposed method employes Student's t-distribution to provide robustness to outliers by modeling the conditional distribution of the privileged information. Experimental results in three publicly available datasets demonstrate the effectiveness of the proposed approach and improve the state-of-The-Art in the LUPI framework for recognizing human activities.
UR - http://www.scopus.com/inward/record.url?scp=85046274083&partnerID=8YFLogxK
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U2 - 10.1109/ICCVW.2017.307
DO - 10.1109/ICCVW.2017.307
M3 - Conference contribution
AN - SCOPUS:85046274083
T3 - Proceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017
SP - 2658
EP - 2665
BT - Proceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 16th IEEE International Conference on Computer Vision Workshops, ICCVW 2017
Y2 - 22 October 2017 through 29 October 2017
ER -