Learning and inferring image segmentations using the GBP typical cut algorithm

Noam Shental, Assaf Zomet, Tomer Hertz, Yair Weiss

Research output: Contribution to journalConference articlepeer-review

32 Scopus citations


Significant progress in image segmentation lias been made by viewing the problem in the framework of graph partitioning. In particular, spectral clustering methods such as "normalized cuts" (ncuts) can efficiently calculate good segmentations using eigenvector calculations. However, spectral methods when applied to images with local connectivity often oversegment homogenous regions. More importantly, they lack a straightforward probabilistic interpretation which makes it difficult to automatically set parameters using training data-in this paper we revisit the typical cut criterion proposed in [1, 5]. We show that computing the typical cut is equivalent to performing inference in an undirected graphical model. This equivalence allows us to use the powerful machinery of graphical models for learning and inferring image segmentations. For inferring segmentations we show that the generalized belief propagation (GBP) algorithm can give excellent results with a runtime that is usually faster than the ncut eigensolver. For learning segmentations we derive a maximum likelihood learning algorithm to learn affinity matrices from labelled datasets. We illustrate both learning and inference on challenging real and synthetic images.

Original languageEnglish
Pages (from-to)1243-1250
Number of pages8
JournalProceedings of the IEEE International Conference on Computer Vision
StatePublished - 1 Jan 2003
Externally publishedYes
Duration: 13 Oct 200316 Oct 2003

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition


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