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We present a novel technique for figure-ground segmentation, where the goal is to separate all foreground objects in a test image from the background. We decompose the test image and all images in a supervised training set into overlapping windows likely to cover foreground objects. The key idea is to transfer segmentation masks from training windows that are visually similar to windows in the test...
We propose a new loss function for discriminative learning of Markov random fields, which is an intermediate loss function between the sequential loss and the pointwise loss. We show this loss function has ldquoMarkov propertyrdquo, that is, the importance of correct labeling for a particular position depends on the numbers of the correct labels around there. This property works to keep local consistencies...
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