FUSION OF IMAGE SEGMENTATION ALGORITHMS USING CONSENSUS CLUSTERING


Ozay M., YARMAN VURAL F. T. , Kulkarni S. R. , Poor H. V.

20th IEEE International Conference on Image Processing (ICIP), Melbourne, Australia, 15 - 18 September 2013, pp.4049-4053 identifier

  • Publication Type: Conference Paper / Full Text
  • City: Melbourne
  • Country: Australia
  • Page Numbers: pp.4049-4053

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.