3 citations · 4 across the 5 of their papers we have counts for
5 papers
RandomBoost: Simplified Multi-class Boosting through Randomization
Sakrapee Paisitkriangkrai, Chunhua Shen, Qinfeng Shi +1
We propose a novel boosting approach to multi-class classification problems, in which multiple classes are distinguished by a set of random projection matrices in essence. The appr…
Training Effective Node Classifiers for Cascade Classification
Chunhua Shen, Peng Wang, Sakrapee Paisitkriangkrai +1
Cascade classifiers are widely used in real-time object detection. Different from conventional classifiers that are designed for a low overall classification error rate, a classifi…
A Direct Approach to Multi-class Boosting and Extensions
Chunhua Shen, Sakrapee Paisitkriangkrai, Anton van den Hengel
Boosting methods combine a set of moderately accurate weaklearners to form a highly accurate predictor. Despite the practical importance of multi-class boosting, it has received fa…
Face Detection with Effective Feature Extraction
Sakrapee Paisitkriangkrai, Chunhua Shen, Jian Zhang
There is an abundant literature on face detection due to its important role in many vision applications. Since Viola and Jones proposed the first real-time AdaBoost based face dete…
Efficiently Learning a Detection Cascade with Sparse Eigenvectors
Chunhua Shen, Sakrapee Paisitkriangkrai, Jian Zhang
In this work, we first show that feature selection methods other than boosting can also be used for training an efficient object detector. In particular, we introduce Greedy Sparse…