Training connectionist networks with queries and selective sampling
Advances in neural information processing systems 2
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
Machine Learning
Machine Learning
Mining of Topographic Feature from Heterogeneous Imagery and Its Application to Lunar Craters
Progress in Discovery Science, Final Report of the Japanese Discovery Science Project
A General Framework for Object Detection
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Robust Real-Time Face Detection
International Journal of Computer Vision
Unsupervised word sense disambiguation rivaling supervised methods
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Learning to Detect Small Impact Craters
WACV-MOTION '05 Proceedings of the Seventh IEEE Workshops on Application of Computer Vision (WACV/MOTION'05) - Volume 1 - Volume 01
Learning on the border: active learning in imbalanced data classification
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Hierarchical sampling for active learning
Proceedings of the 25th international conference on Machine learning
ECML '07 Proceedings of the 18th European conference on Machine Learning
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
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Counting craters is a fundamental task of planetary science, because it provides the only tool for measuring relative ages of planetary surfaces. However, advances in surveying craters present in data gathered by planetary probes have not kept up with advances in data collection. It becomes extremely challenging to automatically count a very large number of small, sub-kilometer size craters in a deluge of high resolution planetary images. In this paper, we combine active learning with semi-supervised learning to build an adaptive learning system to automatically detect craters from high resolution panchromatic planetary images. We propose an Adaptive Selective Algorithm to iteratively enrich an original small training set, using unlabeled test set without additional human labeling effort, to detect craters from a large volume of images. We propose three strategies to improve detection accuracy by integrating classification with exploration on unlabeled samples. The Majority Vote Strategy is used to automatically obtain class labels by exploiting unlabeled samples. The De-Mixed Strategy is used on instance filtering to obtain reliable samples. The Active Stability Strategy is used to obtain an appropriate class distribution in the constructed training set by detecting unstable classes. By using those three strategies, we actively select test instances from test images into an existing small initial training set while rebuilding the classifier in the mean time. Our proposed algorithms are empirically evaluated on a large high resolution Martian image, exhibiting a heavily cratered Martian terrain characterized by heterogeneous surface morphology. The experimental results demonstrate that the proposed approach achieves a higher accuracy than other existing approaches to a large extent.