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.
@article{arxiv.1406.1774,
title = {Small Sample Learning of Superpixel Classifiers for EM Segmentation- Extended Version},
author = {Toufiq Parag and Stephen Plaza and Louis Scheffer},
journal= {arXiv preprint arXiv:1406.1774},
year = {2014}
}