http://dx.doi.org/10.1109/TCSVT.2011.2130270">
 

Document Type

Journal Article

Department/Unit

Department of Computer Science

Title

A boosted co-training algorithm for human action recognition

Language

English

Abstract

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.

Keywords

Co-training, human action recognition, semi-supervised learning

Publication Date

2011

Source Publication Title

IEEE Transactions on Circuits and Systems for Video Technology

Volume

21

Issue

9

Start Page

1203

End Page

1213

Publisher

Institute of Electrical and Electronics Engineers

ISSN (print)

10518215

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