Split-and-merge segmentation of aerial photographs
Computer Vision, Graphics, and Image Processing
Improving Generalization with Active Learning
Machine Learning - Special issue on structured connectionist systems
The nature of statistical learning theory
The nature of statistical learning theory
Remote Sensing Digital Image Analysis: An Introduction
Remote Sensing Digital Image Analysis: An Introduction
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Machine Learning
Machine Learning
Less is More: Active Learning with Support Vector Machines
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Query Learning with Large Margin Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Support vector machine active learning with applications to text classification
The Journal of Machine Learning Research
A survey of image classification methods and techniques for improving classification performance
International Journal of Remote Sensing
International Journal of Remote Sensing
A novel approach to neuro-fuzzy classification
Neural Networks
Novel classification and segmentation techniques with application to remotely sensed images
Transactions on rough sets VII
Color image segmentation using pixel wise support vector machine classification
Pattern Recognition
Arrhythmia classification using local hölder exponents and support vector machine
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
Remote sensing image segmentation by active queries
Pattern Recognition
Computers and Electronics in Agriculture
Computers and Electronics in Agriculture
Active Learning with Bootstrapped Dendritic Classifier applied to medical image segmentation
Pattern Recognition Letters
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The problem of scarcity of labeled pixels, required for segmentation of remotely sensed satellite images in supervised pixel classification framework, is addressed in this article. A support vector machine (SVM) is considered for classifying the pixels into different landcover types. It is initially designed using a small set of labeled points, and subsequently refined by actively querying for the labels of pixels from a pool of unlabeled data. The label of the most interesting/ ambiguous unlabeled point is queried at each step. Here, active learning is exploited to minimize the number of labeled data used by the SVM classifier by several orders. These features are demonstrated on an IRS-1A four band multi-spectral image. Comparison with related methods is made in terms of number of data points used, computational time and a cluster quality measure.