Search Results for author: Sakrapee Paisitkriangkrai

Found 10 papers, 0 papers with code

Structured learning of metric ensembles with application to person re-identification

no code implementations27 Nov 2015 Sakrapee Paisitkriangkrai, Lin Wu, Chunhua Shen, Anton Van Den Hengel

However, seeking an optimal combination of visual features which is generic yet adaptive to different benchmarks is a unsoved problem, and metric learning models easily get over-fitted due to the scarcity of training data in person re-identification.

Metric Learning Person Re-Identification

Learning to rank in person re-identification with metric ensembles

no code implementations CVPR 2015 Sakrapee Paisitkriangkrai, Chunhua Shen, Anton Van Den Hengel

We propose an effective structured learning based approach to the problem of person re-identification which outperforms the current state-of-the-art on most benchmark data sets evaluated.

Learning-To-Rank Person Re-Identification

Pedestrian Detection with Spatially Pooled Features and Structured Ensemble Learning

no code implementations18 Sep 2014 Sakrapee Paisitkriangkrai, Chunhua Shen, Anton Van Den Hengel

Experimental results on both synthetic and real-world data sets demonstrate the effectiveness of our approach, and we show that it is possible to train state-of-the-art pedestrian detectors using the proposed structured ensemble learning method with spatially pooled features.

Ensemble Learning Object Detection +1

Strengthening the Effectiveness of Pedestrian Detection with Spatially Pooled Features

no code implementations3 Jul 2014 Sakrapee Paisitkriangkrai, Chunhua Shen, Anton Van Den Hengel

The combination of these factors leads to a pedestrian detector which outperforms all competitors on all of the standard benchmark datasets.

Pedestrian Detection

Efficient pedestrian detection by directly optimize the partial area under the ROC curve

no code implementations3 Oct 2013 Sakrapee Paisitkriangkrai, Chunhua Shen, Anton Van Den Hengel

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.

Ensemble Learning Object Detection +1

A scalable stage-wise approach to large-margin multi-class loss based boosting

no code implementations21 Jul 2013 Sakrapee Paisitkriangkrai, Chunhua Shen, Anton Van Den Hengel

In this work, we propose a scalable and simple stage-wise multi-class boosting method, which also directly maximizes the multi-class margin.

General Classification Multi-class Classification

Asymmetric Pruning for Learning Cascade Detectors

no code implementations25 Mar 2013 Sakrapee Paisitkriangkrai, Chunhua Shen, Anton Van Den Hengel

In this work, we present a new approach to train an effective node classifier in a cascade detector.

Real-Time Object Detection

Face Detection with Effective Feature Extraction

no code implementations29 Sep 2010 Sakrapee Paisitkriangkrai, Chunhua Shen, Jian Zhang

There is an abundant literature on face detection due to its important role in many vision applications.

Face Detection

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