The nature of statistical learning theory
The nature of statistical learning theory
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Neural Network-Based Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Example-Based Learning for View-Based Human Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust recognition using eigenimages
Computer Vision and Image Understanding - Special issue on robusst statistical techniques in image understanding
Linear Time Euclidean Distance Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Robust PCA Algorithm for Building Representations from Panoramic Images
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
A Framework for Robust Subspace Learning
International Journal of Computer Vision - Special Issue on Computational Vision at Brown University
Unsupervised Improvement of Visual Detectors using Co-Training
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Detecting Pedestrians Using Patterns of Motion and Appearance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Weighted and Robust Incremental Method for Subspace Learning
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Learning to Detect Objects in Images via a Sparse, Part-Based Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning object detection from a small number of examples: the importance of good features
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
An unsupervised, online learning framework for moving object detection
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Recognizing cars in aerial imagery to improve orthophotos
Proceedings of the 15th annual ACM international symposium on Advances in geographic information systems
Long term carefully learning for person detection application to intelligent surveillance system
Proceedings of the 9th International Conference on Advances in Mobile Computing and Multimedia
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We present a novel framework for unsupervised training of an object detection system. The basic idea is to (1) exploit a huge amount of unlabeled video data by being very conservative in selecting training examples; and (2) to start with a very simple object detection system and using generative and discriminative classifiers in an iterative co-training fashion to arrive at increasingly better object detectors. We demonstrate the framework on a surveillance task where we learn a person detector. We start with a simple moving object classifier and proceed with robust PCA (on shape and appearance) as a generative classifier which in turn generates a training set for a discriminative AdaBoost classifier. The results obtained by AdaBoost are again filtered by PCA which produces an even better training set. We demonstrate that by using this approach we avoid hand labeling training data and still achieve a state of the art detection rate.