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'st-density function, which is naturally …

In this work, a novel method based on the learning using privileged information (LUPI)
paradigm for recognizing complex human activities is proposed that handles missing
information during testing. We present a supervised probabilistic approach that integrates
LUPI into a hidden conditional random field (HCRF) model. The proposed model is called
HCRF+ and may be trained using both maximum likelihood and maximum margin
approaches. It employs a self-training technique for automatic estimation of the …