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School of Computer Science
Level 4
Ingkarni Wardli Building
THE UNIVERSITY OF ADELAIDE
SA 5005
AUSTRALIA
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Sakrapee (Paul) Paisitkriangkrai
Postdoctoral Research Fellow
The Australian Center for Visual Technologies
School of Computer Science, The University of Adelaide, Australia

Level 5, Innova 21, The University of Adelaide, Adelaide, SA 5006, Australia
Phone: +61 8 8313 0282
Email: paulp(at)cs(dot)adelaide(dot)edu(dot)au
Google Scholar: http://scholar.google.com.au/citations?hl=en&user=lNqlhO4AAAAJ
Web: http://www.cs.adelaide.edu.au/~paulp



Research Projects

Online learning

Although, offline object detectors have shown a tremendous success. One major drawback of offline techniques is that a complete set of training data has to be collected beforehand. In addition, once learned, an offline detector cannot make use of newly arriving data. In order to alleviate these shortcomings, online learning has been adopted with following objectives:- the technique should be computational and storage efficient; and the updated classifier must maintain its high classification accuracy.

Improving upon GSLDA classifier, an effective and efficient framework for learning an online GSLDA model is proposed. Unlike existing online object detection algorithms, e.g., Grabner and Bischof or Pham and Cham, our online approach makes use of LDA's learning criterion which has been shown in our previous experiment to outperform the AdaBoost's learning criterion for offline object detection task. Our updating algorithm is very efficient since we neither replace weak learners nor throw away any weak learners during updating phase. Finally, we adopt a learning technique similar to semi-supervised learning where the classifier makes use of the unlabeled data in conjunction with a small amount of labeled data.

 


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