Department of Computer Science
A boosted co-training algorithm for human action recognition
This paper proposes a boosted co-training algorithm for human action recognition. To address the view-sufficiency and view-dependency issues in co-training, two new confidence measures, namely, inter-view confidence and intra-view confidence, are proposed. They are dynamically fused into a semi-supervised learning process. Mutual information is employed to quantify the inter-view uncertainty and measure the independence among respective views. Intra-view confidence is estimated from boosted hypotheses to measure the total data inconsistency of labeled data and unlabeled data. Given a small set of labeled videos and a large set of unlabeled videos, the proposed semi-supervised learning algorithm trains a classifier by maximizing the inter-view confidence and intra-view confidence, and dynamically incorporating unlabeled data into the labeled data set. To evaluate the proposed boosted co-training algorithm, eigen-action and information saliency feature vectors are employed as two input views. The KTH and Weizmann human action databases are used for experiments, average recognition accuracy of 93.2% and 99.6% are obtained, respectively. © 2011 IEEE.
Co-training, human action recognition, semi-supervised learning
Source Publication Title
IEEE Transactions on Circuits and Systems for Video Technology
Institute of Electrical and Electronics Engineers
Liu, Chang, and Pong C. Yuen. "A boosted co-training algorithm for human action recognition." IEEE Transactions on Circuits and Systems for Video Technology 21.9 (2011): 1203-1213.