Weakly Supervised Learning of Image Partitioning Using Decision Trees with Structured Split Criteria

Abstract

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… (More)
DOI: 10.1109/ICCV.2013.232

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