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School of Computer Science
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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

Web: http://www.cs.adelaide.edu.au/~paulp



Research Interest

I am interested in computer vision and machine learning. I have been working on real-time visual detector and large-scale multimedia. I am currently working on train simulation project at the Australian Center for Visual Technologies (ACVT), the University of Adelaide under the supervision of Professor Anton van den Hengel.

The ACVT conducts a challenging and difficult research in the area of image processing, computer vision and machine learning. Our objective is to design a new technology that can match the full capability of human vision.


Publication

A list of publications.

Research Projects

Binary-class classification

Optimizing the partial area under the ROC curve

We propose a novel ensemble learning method which achieves a maximal detection rate at a user-defined range of false positive rates by directly optimizing the partial AUC using structured learning.

S. Paisitkriangkrai, C. Shen and A. van den Hengel. Efficient Pedestrian Detection by Directly Optimizing the Partial Area under the ROC Curve, ICCV 2013, PDF Supplementary

AdaBoostpAUCEns (ours)

Greedy Sparse Linear Discriminant Analysis (GSLDA)

We introduce Greedy Sparse Linear Discriminant Analysis (GSLDA) for its conceptual simplicity and computational efficiency to the task of face and pedestrian detection.

S. Paisitkriangkrai, C. Shen and J. Zhang. Sharing features in multi-class boosting via group sparsity, CVPR 2009, PDF Journal

AdaBoostGSLDA (ours)

Multi-class classification

Multi-class Boosting + Group sparsity = Feature sharing classifier

We present a novel formulation of fully corrective boosting for multi-class classification problems with the awareness of sharing features.

S. Paisitkriangkrai, C. Shen and A. van den Hengel. Sharing features in multi-class boosting via group sparsity, CVPR 2012, PDF Supplementary

AdaBoost.ECCOur MultiBoost (group)

Multi-class boosting with Crammer and Singer's formulation

We propose scalable and effective classification model for multi-class boosting classification. The approach directly maximizes the multi-class margin (similar to the multi-class SVM (MSVM) formulation of Crammer and Singer).

The technical paper and source code for MultiBoost (Stage-wise) can be downloaded here. PDF source code.

MultiBoost (Stage-wise) 

Randomized boosting with matlab source code

The new model trains a single-vector parameterized classifier irrespective of the number of classes. The advantage of the new appraoch is that multi-class boosting can be trained at the same learning complexity of binary boosting.

The technical paper and source code for RandomBoost can be downloaded here. PDF source code.

Data distributionRandomBoost

Online learning

Large-scale multimedia framework

Clip-based Hierarchical Representation for Near-Duplicate Video Detection



Binary-class visual detector

Pedestrian detection


Machine learning demo

Regression 5A

Regression 5B

Linear regression

Voronoi Diagram / Delaunay Triangulation

Weka download (java -Xmx1000M -jar weka.jar)

 


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