title: Small sample learning of superpixel classifiers for EM segmentation.
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.
authors.forename: Toufiq
authors.initials: T
authors.lastname: Parag
authors.forename: Stephen
authors.initials: S
authors.lastname: Plaza
authors.forename: Louis
authors.initials: L
authors.lastname: Scheffer
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D002478:Cells, Cultured; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D046529:Microscopy, Electron, Transmission / Q000379:methods*; D016501:Neurites / Q000648:ultrastructure*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D018401:Sample Size; D012680:Sensitivity and Specificity