Fusion of image segmentation algorithms using consensus clustering

Mete Ozay, Fatos T Yarman Vural, Sanjeev R. Kulkarni, H. Vincent Poor

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

5 Scopus citations

Abstract

A new segmentation fusion method is proposed that ensembles the output of several segmentation algorithms applied on a remotely sensed image. The candidate segmentation sets are processed to achieve a consensus segmentation using a stochastic optimization algorithm based on the Filtered Stochastic BOEM (Best One Element Move) method. For this purpose, Filtered Stochastic BOEM is reformulated as a segmentation fusion problem by designing a new distance learning approach. The proposed algorithm also embeds the computation of the optimum number of clusters into the segmentation fusion problem.

Original languageEnglish (US)
Title of host publication2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings
Pages4049-4053
Number of pages5
DOIs
StatePublished - Dec 1 2013
Event2013 20th IEEE International Conference on Image Processing, ICIP 2013 - Melbourne, VIC, Australia
Duration: Sep 15 2013Sep 18 2013

Publication series

Name2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings

Other

Other2013 20th IEEE International Conference on Image Processing, ICIP 2013
CountryAustralia
CityMelbourne, VIC
Period9/15/139/18/13

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition

Keywords

  • Segmentation
  • clustering
  • consensus
  • fusion
  • stochastic optimization

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    Ozay, M., Vural, F. T. Y., Kulkarni, S. R., & Poor, H. V. (2013). Fusion of image segmentation algorithms using consensus clustering. In 2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings (pp. 4049-4053). [6738834] (2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings). https://doi.org/10.1109/ICIP.2013.6738834