Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Small Sample Learning of Superpixel Classifiers for EM Segmentation-
  Extended Version   Pixel and superpixel classifiers have become essential tools for EM
segmentation algorithms. Training these classifiers remains a major bottleneck
primarily due to the requirement of completely annotating the dataset which is
tedious, error-prone and costly. In this paper, we propose an interactive
learning scheme for the superpixel classifier for EM segmentation. Our
algorithm is "active semi-supervised" because it requests the labels of a small
number of examples from user and applies label propagation technique to
generate these queries. Using only a small set ($<20\%$) of all datapoints, the
proposed algorithm consistently generates a classifier almost as accurate as
that estimated from a complete groundtruth. We provide segmentation results on
multiple datasets to show the strength of these classifiers.

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The categories of this paper are: astro-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: nucl-ex
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The categories of this paper are: eess
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The categories of this paper are: adap-org
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The categories of this paper are: hep-th
28
The categories of this paper are: alg-geom
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The categories of this paper are: stat
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The categories of this paper are: atom-ph
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The categories of this paper are: cmp-lg
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The categories of this paper are: q-fin
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The categories of this paper are: econ
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The categories of this paper are: quant-ph
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The categories of this paper are: plasm-ph
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The categories of this paper are: chao-dyn
30
The categories of this paper are: q-alg