A boosted co-training algorithm for human action recognition

Chang Liu*, Pong Chi YUEN

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

39 Citations (Scopus)

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.

Original languageEnglish
Article number5739520
Pages (from-to)1203-1213
Number of pages11
JournalIEEE Transactions on Circuits and Systems for Video Technology
Volume21
Issue number9
DOIs
Publication statusPublished - Sep 2011

Scopus Subject Areas

  • Media Technology
  • Electrical and Electronic Engineering

User-Defined Keywords

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

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