A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Information-based objective functions for active data selection
Neural Computation
A sequential algorithm for training text classifiers
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Improving Generalization with Active Learning
Machine Learning - Special issue on structured connectionist systems
Multimedia Systems - Special issue on content-based retrieval
Machine Learning
Machine Learning
Machine Learning
Toward Optimal Active Learning through Sampling Estimation of Error Reduction
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Query Learning Strategies Using Boosting and Bagging
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Employing EM and Pool-Based Active Learning for Text Classification
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 1) - Volume 1
Improved Rooftop Detection in Aerial Images with Machine Learning
Machine Learning
Support vector machine active learning with applications to text classification
The Journal of Machine Learning Research
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
A Survey of Outlier Detection Methodologies
Artificial Intelligence Review
Individual Recognition Using Gait Energy Image
IEEE Transactions on Pattern Analysis and Machine Intelligence
Category detection using hierarchical mean shift
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Reducing labeling effort for structured prediction tasks
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Incremental one-class learning with bounded computational complexity
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Active learning for constrained Dirichlet process mixture models
GEMS '10 Proceedings of the 2010 Workshop on GEometrical Models of Natural Language Semantics
Finding rare classes: adapting generative and discriminative models in active learning
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stream-based joint exploration-exploitation active learning
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
International Journal of Computer Vision
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Classification is used to solve countless problems. Many real world computer vision problems, such as visual surveillance, contain uninteresting but common classes alongside interesting but rare classes. The rare classes are often unknown, and need to be discovered whilst training a classifier. Given a data set active learning selects the members within it to be labelled for the purpose of constructing a classifier, optimising the choice to get the best classifier for the least amount of effort. We propose an active learning method for scenarios with unknown, rare classes, where the problems of classification and rare class discovery need to be tackled jointly. By assuming a non-parametric prior on the data the goals of new class discovery and classification refinement are automatically balanced, without any tunable parameters. The ability to work with any specific classifier is maintained, so it may be used with the technique most appropriate for the problem at hand. Results are provided for a large variety of problems, demonstrating superior performance.