Small sample learning of superpixel classifiers for EM segmentation- extended version.

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arXiv. 2014 Jun 6;:arXiv:1406.1774 [cs.CV]

Small sample learning of superpixel classifiers for EM segmentation- extended version. Scheffer LabFlyEM

Parag T, Plaza SM, Scheffer LK

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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.

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Small sample learning of superpixel classifiers for EM segmentation- extended version.

Abstract

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