Combining clusterings with different detail levels

Oded Kaminsky, Jacob Goldberger

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this study we address the problem of recovering a clustering of a dataset based on several clusterings provided by different experts. These experts provide clusterings on different levels (coarser or finer than the others). We present an automatic algorithm that combines the information provided by the experts into a single clustering that can be viewed as the average point of the input clusterings. We formulate the problem as an instance of correlation clustering and apply integer linear programming to obtain the average clustering. As a byproduct, we also obtain for each expert its reliability and the detail level encoded in its clustering. We apply the proposed algorithm to the task of averaging several image segmentations. The average segmentation is efficiently computed by first grouping the image into superpixels and then applying the proposed algorithm on the superpixel map. The performance of the proposed algorithm is demonstrated on manually annotated images from the Berkeley segmentation dataset.

Original languageEnglish
Title of host publication2016 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings
EditorsKostas Diamantaras, Aurelio Uncini, Francesco A. N. Palmieri, Jan Larsen
PublisherIEEE Computer Society
ISBN (Electronic)9781509007462
DOIs
StatePublished - 8 Nov 2016
Event26th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings - Vietri sul Mare, Salerno, Italy
Duration: 13 Sep 201616 Sep 2016

Publication series

NameIEEE International Workshop on Machine Learning for Signal Processing, MLSP
Volume2016-November

Conference

Conference26th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings
Country/TerritoryItaly
CityVietri sul Mare, Salerno
Period13/09/1616/09/16

Keywords

  • Ensemble decision
  • ILP
  • segmentation

All Science Journal Classification (ASJC) codes

  • Human-Computer Interaction
  • Signal Processing

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