Proceedings of the 2009 ACM symposium on Applied Computing
Data clustering as an optimum-path forest problem with applications in image analysis
International Journal of Imaging Systems and Technology - Contemporary Challenges in Combinatorial Image Analysis
Active learning with statistical models
Journal of Artificial Intelligence Research
Efficient supervised optimum-path forest classification for large datasets
Pattern Recognition
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The labor-intensive and time-consuming process of annotating data is a serious bottleneck in many pattern recognition applications when handling massive datasets. Active learning strategies have been sought to reduce the cost on human annotation, by means of automatically selecting the most informative unlabeled samples for annotation. The critical issue lies on the selection of such samples. As an effective solution, we propose an active learning approach that preprocesses the dataset, efficiently reduces and organizes a learning set of samples and selects the most representative ones for human annotation. Experiments performed on real datasets show that the proposed approach requires only a few iterations to achieve high accuracy, keeping user involvement to a minimum.