Algorithms for clustering data
Algorithms for clustering data
Improving Generalization with Active Learning
Machine Learning - Special issue on structured connectionist systems
Classifying Hyperplanes in Hypercubes
SIAM Journal on Discrete Mathematics
Unsupervised Texture Segmentation in a Deterministic Annealing Framework
IEEE Transactions on Pattern Analysis and Machine Intelligence
Remote Sensing Digital Image Analysis: An Introduction
Remote Sensing Digital Image Analysis: An Introduction
Markov Random Field Models for Unsupervised Segmentation of Textured Color Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Performance Evaluation of Some Clustering Algorithms and Validity Indices
IEEE Transactions on Pattern Analysis and Machine Intelligence
Less is More: Active Learning with Support Vector Machines
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Normalized Cuts and Image Segmentation
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Support vector machine active learning with applications to text classification
The Journal of Machine Learning Research
Segmentation of multispectral remote sensing images using active support vector machines
Pattern Recognition Letters
Active Learning to Recognize Multiple Types of Plankton
The Journal of Machine Learning Research
Rapid and brief communication: Active learning for image retrieval with Co-SVM
Pattern Recognition
Active semi-supervised fuzzy clustering
Pattern Recognition
SVM-based active feedback in image retrieval using clustering and unlabeled data
Pattern Recognition
Hierarchical sampling for active learning
Proceedings of the 25th international conference on Machine learning
An experimental comparison of performance measures for classification
Pattern Recognition Letters
Expert Systems with Applications: An International Journal
Recent advances in remote sensing image processing
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Characterization, Stability and Convergence of Hierarchical Clustering Methods
The Journal of Machine Learning Research
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Active learning methods for electrocardiographic signal classification
IEEE Transactions on Information Technology in Biomedicine
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Active learning deals with developing methods that select examples that may express data characteristics in a compact way. For remote sensing image segmentation, the selected samples are the most informative pixels in the image so that classifiers trained with reduced active datasets become faster and more robust. Strategies for intelligent sampling have been proposed with model-based heuristics aiming at the search of the most informative pixels to optimize model's performance. Unlike standard methods that concentrate on model optimization, here we propose a method inspired in the cluster assumption that holds in most of the remote sensing data. Starting from a complete hierarchical description of the data, the proposed strategy aims at sampling and labeling pixels in order to discover the data partitioning that best matches with the user's expected classes. Thus, the method combines active supervised and unsupervised clustering with a smart prune-and-label strategy. The proposed method is successfully evaluated in two challenging remote sensing scenarios: hyperspectral and very high spatial resolution (VHR) multispectral images segmentation.