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Small Sample Learning of Superpixel Classifiers for EM Segmentation

  • Toufiq Parag
  • Stephen Plaza
  • Louis Scheffer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8673)

Abstract

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.

Keywords

Label Propagation Active Learning Algorithm arXiv Version Label Propagation Method Motion Detection Circuit 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Toufiq Parag
    • 1
  • Stephen Plaza
    • 1
  • Louis Scheffer
    • 1
  1. 1.Janelia Farm Research Campus- HHMIAshburnUSA

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