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
  • Middle East Technical University Affiliated: Yes

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.