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Fusion of Image Segmentation Algorithms using Consensus Clustering

Author(s): Ozay, Mete; Vural, Fatos T Yarman; Kulkarni, Sanjeev R; Poor, H Vincent

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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.
Publication Date: 2013
Citation: Ozay, Mete, Vural, Fatos T Yarman, Kulkarni, Sanjeev R, Poor, H Vincent. Fusion of Image Segmentation Algorithms using Consensus Clustering. 20th IEEE International Conference on Image Processing (ICIP), 4049-4053, Melbourne, VIC, 15-18 Sept. 2013, 10.1109/ICIP.2013.6738834
DOI: doi:10.1109/ICIP.2013.6738834
ISSN: 1522-4880
EISSN: 2381-8549
Pages: 4049-4053
Type of Material: Conference Article
Journal/Proceeding Title: IEEE International Conference on Image Processing (ICIP)
Version: Author's manuscript

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