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NIPS2009_0084_slide - to directly minimize a...

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84. Maximin afFnity learning of image segmentation 1. Some local errors in boundary detection can have dramatic global consequences noisy image desired boundary predicted boundary (segmentation consistent) predicted boundary (segmentation inconsistent) catastrophic minor nuisance minor nuisance Boundary detectors for image segmentation are usually trained to minimize local boundary classiFcation error. But some local errors in boundary detection can have dramatic global consequences. We show how to train a boundary detector
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Unformatted text preview: to directly minimize a clustering-based global segmentation error, resulting in end-to-end segmentation learning . 2. Rand index measures global segmentation error Abstract end-to-end segmentation learning P P P lowest pixel minimum pixel along a path maximum over paths of the minimum pixel 3. Maximin paths are used to directly minimize global segmentation error 1 2 3 4 1 2 3 4 1’ 2’ 3’ 4’ 3 1’ 2’ 3’ 4’ merger split rand index groundtruth test all pairs!...
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