FUSION OF IMAGE SEGMENTATION ALGORITHMS USING CONSENSUS CLUSTERING

Date

2013-09-18

Author

Ozay, Mete
Yarman Vural, Fatoş Tunay
Kulkarni, Sanjeev R.
Poor, H. Vincent

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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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.

Subject Keywords

Segmentation, Clustering, Fusion, Consensus, Stochastic optimization

URI

https://hdl.handle.net/11511/53721

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Department of Computer Engineering, Conference / Seminar