Ensemble segmentation using efficient integer linear programming

Amir Alush, Jacob Goldberger

Research output: Contribution to journalArticlepeer-review

41 Scopus citations


We present a method for combining several segmentations of an image into a single one that in some sense is the average segmentation in order to achieve a more reliable and accurate segmentation result. The goal is to find a point in the "space of segmentations" which is close to all the individual segmentations. We present an algorithm for segmentation averaging. The image is first oversegmented into superpixels. Next, each segmentation is projected onto the superpixel map. An instance of the EM algorithm combined with integer linear programming is applied on the set of binary merging decisions of neighboring superpixels to obtain the average segmentation. Apart from segmentation averaging, the algorithm also reports the reliability of each segmentation. The performance of the proposed algorithm is demonstrated on manually annotated images from the Berkeley segmentation data set and on the results of automatic segmentation algorithms.

Original languageEnglish
Article number6122028
Pages (from-to)1966-1977
Number of pages12
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Issue number10
StatePublished - 2012


  • EM algorithm
  • Image segmentation
  • correlation clustering
  • ensemble segmentation
  • integer linear programming


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