Learning to Recognize Plankton
T. Luo, K. Kkramer, D. Goldgof, L. Hall, S. Samson, A
Remsen, T. Hopkins
Journal of Machone Learning Research
JMLR 6 April 2005, pages 589-613
This paper presents an active learning method to reduce domain experts'
labeling effort in applying 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 least certainty active learning method and random sampling.
It can also run in batch mode with an accuracy comparable to labeling one image
at a time and retraining.
There are two data sets used in the paper. MasterTestImages and
ValidationImages. They are both availabe in 3 different formats C45, ARFF, and
Sparse. The ARFF version of the data sets includes the image file name of the
plankton for each example. The images that the two data sets are derived from
are available in two seperate zip files, one for test and the other for