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We introduce a multiple instance learning algorithm based on randomized decision trees. Our model extends an existing algorithm by Bloc keel et al. [2] in several ways: 1) We learn a random forest instead of a single tree. 2) We construct the trees by splits based on non-linear boundaries on multiple features at a time. 3) We learn an optimal way of combining the decisions of multiple trees under...
We propose a scheme that allows to partition an image into a previously unknown number of segments, using only minimal supervision in terms of a few must-link and cannot-link annotations. We make no use of regional data terms, learning instead what constitutes a likely boundary between segments. Since boundaries are only implicitly specified through cannot-link constraints, this is a hard and nonconvex...
We present the first variational framework for multi-label segmentation on the ray space of 4D light fields. For traditional segmentation of single images, features need to be extracted from the 2D projection of a three-dimensional scene. The associated loss of geometry information can cause severe problems, for example if different objects have a very similar visual appearance. In this work, we show...
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