Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Multiclass learning, boosting, and error-correcting codes
COLT '99 Proceedings of the twelfth annual conference on Computational learning theory
Machine Learning
Unsupervised Improvement of Visual Detectors using Co-Training
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Robust Fragments-based Tracking using the Integral Histogram
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Large Scale Multiple Kernel Learning
The Journal of Machine Learning Research
Incremental Learning for Robust Visual Tracking
International Journal of Computer Vision
The value of agreement a new boosting algorithm
Journal of Computer and System Sciences
An RKHS for multi-view learning and manifold co-regularization
Proceedings of the 25th international conference on Machine learning
SERBoost: Semi-supervised Boosting with Expectation Regularization
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Semi-supervised On-Line Boosting for Robust Tracking
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Semi-Supervised Learning
Multi-view discriminative sequential learning
ECML'05 Proceedings of the 16th European conference on Machine Learning
A boosting approach to multiview classification with cooperation
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
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Many learning tasks for computer vision problems can be described by multiple views or multiple features. These views can be exploited in order to learn from unlabeled data, a.k.a. "multi-view learning". In these methods, usually the classifiers iteratively label each other a subset of the unlabeled data and ignore the rest. In this work, we propose a new multi-view boosting algorithm that, unlike other approaches, specifically encodes the uncertainties over the unlabeled samples in terms of given priors. Instead of ignoring the unlabeled samples during the training phase of each view, we use the different views to provide an aggregated prior which is then used as a regularization term inside a semisupervised boosting method. Since we target multi-class applications, we first introduce a multi-class boosting algorithm based on maximizing the mutli-class classification margin. Then, we propose our multi-class semisupervised boosting algorithm which is able to use priors as a regularization component over the unlabeled data. Since the priors may contain a significant amount of noise, we introduce a new loss function for the unlabeled regularization which is robust to noisy priors. Experimentally, we show that the multi-class boosting algorithms achieves state-of-theart results in machine learning benchmarks. We also show that the new proposed loss function is more robust compared to other alternatives. Finally, we demonstrate the advantages of our multi-view boosting approach for object category recognition and visual object tracking tasks, compared to other multi-view learning methods.