@article {3846,
	title = {Learning to agglomerate superpixel hierarchies.},
	journal = {Neural Information Processing Systems},
	volume = {24},
	year = {2011},
	month = {2011},
	pages = {648-56},
	abstract = {<p>An agglomerative clustering algorithm merges the most similar pair of clusters at every iteration. The function that evaluates similarity is traditionally handdesigned, but there has been recent interest in supervised or semisupervised settings in which ground-truth clustered data is available for training. Here we show how to train a similarity function by regarding it as the action-value function of a reinforcement learning problem. We apply this general method to segment images by clustering superpixels, an application that we call Learning to Agglomerate Superpixel Hierarchies (LASH). When applied to a challenging dataset of brain images from serial electron microscopy, LASH dramatically improved segmentation accuracy when clustering supervoxels generated by state of the boundary detection algorithms. The naive strategy of directly training only supervoxel similarities and applying single linkage clustering produced less improvement.</p>
},
	author = {Jain, Viren and Turaga, Srinivas and Briggman, Kevin and Helmstaedter, Moritz N. and Denk, Winfried and Seung, Sebastian}
}
@conference {3858,
	title = {Boundary learning by optimization with topological constraints.},
	booktitle = {IEEE Conference on Computer Vision and Pattern Recognition},
	year = {2010},
	month = {2010},
	abstract = {<p>Recent studies have shown that machine learning can improve the accuracy of detecting object boundaries in images. In the standard approach, a boundary detector is trained by minimizing its pixel-level disagreement with human boundary tracings. This naive metric is problematic because it is overly sensitive to boundary locations. This problem is solved by metrics provided with the Berkeley Segmentation Dataset, but these can be insensitive to topological differences, such as gaps in boundaries. Furthermore, the Berkeley metrics have not been useful as cost functions for supervised learning. Using concepts from digital topology, we propose a new metric called the warping error that tolerates disagreements over boundary location, penalizes topological disagreements, and can be used directly as a cost function for learning boundary detection, in a method that we call Boundary Learning by Optimization with Topological Constraints (BLOTC). We trained boundary detectors on electron microscopic images of neurons, using both BLOTC and standard training. BLOTC produced substantially better performance on a 1.2 million pixel test set, as measured by both the warping error and the Rand index evaluated on segmentations generated from the boundary labelings. We also find our approach yields significantly better segmentation performance than either gPb-OWT-UCM or multiscale normalized cut, as well as Boosted Edge Learning trained directly on our data.</p>
},
	author = {Jain, Viren and Bollmann, Benjamin and Richardson, Mark and Berger, Daniel R. and Helmstaedter, Moritz N. and Briggman, Kevin L and Denk, Winfried and Bowden, Jared B. and Mendenhall, John M. and Abraham, Wickliffe C. and Harris, Kristen M and Kasthuri, Narayanan and Hayworth, Ken J. and Schalek, Richard and Tapia, Juan Carlos and Lichtman, Jeff W. and Seung, Hyunjune Sebastian}
}
@proceedings {4528,
	title = {Supervised learning of image restoration with convolutional networks.},
	volume = {2},
	year = {2007},
	month = {2007 Oct 14},
	pages = {1-8},
	publisher = {IEEE},
	edition = {ICCV},
	author = {Jain, Viren and Murray, Joseph F and Roth, Fabian and Turaga, Srinivas and Zhigulin, Valentin and Briggman, Kevin L and Helmstaedter, Moritz N. and Denk, Winfried and Seung, H Sebastian}
}
