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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

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.

Original image	The ACVT objective

Publication

Research Projects

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.ECC	Our 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 distribution	RandomBoost

Binary-class classification

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

AdaBoost	GSLDA (ours)

Online learning

Large-scale multimedia framework

Clip-based Hierarchical Representation for Near-Duplicate Video Detection

Binary-class visual detector

Pedestrian detection