Learning to approximate global shape priors for figure-ground segmentation

Daniel Küttel, Vittorio Ferrari

Research output: Chapter in Book/Report/Conference proceedingConference contribution


We present a technique for approximate minimization of two-label energy functions with higher-order or global potentials. Our method treats the energy function as a black-box: it does not exploit knowledge of its form nor its order, as opposed to optimization schemes specialized to a particular form. The key idea is to automatically learn a lower-order approximation of the energy function, which can then be minimized used existing efficient algorithms. We experimentally demonstrate our method for binary image seg-mentation, where it enables to incorporate a global shape prior into traditional models based on pairwise conditional random fields.
Original languageEnglish
Title of host publicationProceedings of the British Machine Vision Conference
PublisherBMVA Press
Number of pages12
Publication statusPublished - 2013

Fingerprint Dive into the research topics of 'Learning to approximate global shape priors for figure-ground segmentation'. Together they form a unique fingerprint.

Cite this