Fusion of Image Segmentation Algorithms using Consensus Clustering
Computer Vision and Pattern Recognition
2016-11-17 v1
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.
Cite
@article{arxiv.1502.05435,
title = {Fusion of Image Segmentation Algorithms using Consensus Clustering},
author = {Mete Ozay and Fatos T. Yarman Vural and Sanjeev R. Kulkarni and H. Vincent Poor},
journal= {arXiv preprint arXiv:1502.05435},
year = {2016}
}
Comments
A version of the manuscript was published in ICIP 2013