Fusion of Image Segmentation Algorithms using Consensus Clustering
This work addresses the challenge of combining segmentation algorithms for more accurate remote sensing image analysis, representing an incremental improvement in the field.
The authors tackled the problem of fusing multiple image segmentation outputs into a consensus segmentation using a stochastic optimization method, achieving improved results in remote sensing applications with specific performance gains reported.
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.