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- W1266862150 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." @default.
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- W1266862150 date "2014-01-01" @default.
- W1266862150 modified "2023-09-26" @default.
- W1266862150 title "Small Sample Learning of Superpixel Classifiers for EM Segmentation" @default.
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- W1266862150 doi "https://doi.org/10.1007/978-3-319-10404-1_49" @default.
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