Segmentation Ensemble via Kernels

Clustering ensemble is a promising technique to face data clustering problems. Similarly, the combination of different segmentations to obtain a consensus one could be a powerful tool for addressing image segmentation problems. Such segmentation ensemble algorithms should be able to deal with the possible large image size and should preserve the spatial relation among pixels in the image. In this paper, we formalize the segmentation ensemble problem and introduce a new method to solve it, which is based on the kernel clustering ensemble philosophy. We prove that the Rand index is a kernel function and we use it as similarity measure between segmentations in the proposed algorithm. This algorithm is experimentally evaluated on the Berkeley image database and compared to several state-of-the-art clustering ensemble algorithms. The achieved results ratify the accuracy of our proposal.