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
Link to Publisher's Edition
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.