Active Learning to Recognize Multiple Types of Plankton
Tong Luo, Kurt Kramer, Dmitry B. Goldgof, Lawrence O. Hall, Scott Samson, Andrew Remsen, Thomas Hopkins; 6(20):589−613, 2005.
This paper presents an active learning method which reduces the labeling effort of domain experts in multi-class classification problems. Active learning is applied in conjunction with support vector machines to recognize underwater zooplankton from higher-resolution, new generation SIPPER II images. Most previous work on active learning with support vector machines only deals with two class problems. In this paper, we propose an active learning approach "breaking ties" for multi-class support vector machines using the one-vs-one approach with a probability approximation. Experimental results indicate that our approach often requires significantly less labeled images to reach a given accuracy than the approach of labeling the least certain test example and random sampling. It can also be applied in batch mode resulting in an accuracy comparable to labeling one image at a time and retraining.
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