@proceedings {46298,
	title = {Small sample learning of superpixel classifiers for EM segmentation.},
	volume = {17},
	year = {2014},
	month = {2014},
	pages = {389-97},
	abstract = {<p>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 {\textquoteright}active semi-supervised{\textquoteright} 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 (\&lt; 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.</p>
},
	keywords = {Algorithms, Artificial Intelligence, Cells, Cultured, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Imaging, Three-Dimensional, Microscopy, Electron, Transmission, Neurites, Pattern Recognition, Automated, Reproducibility of Results, Sample Size, Sensitivity and Specificity},
	author = {Parag, Toufiq and Plaza, Stephen and Scheffer, Louis}
}
