Iterative ensemble normalized cuts
We provide a novel interpretation of the gap-view concept in NCut. We improve NCut by assembling out-of-sample extensions of multiple training sets. We speed up the clustering method by employing an iterative algorithm. Our proposed algorithm performs better than the state-of-the-art algorithms.
iterative ensemble ncut gap-normalized distance spectral clustering image segmentation