Kernel Cut

swMATH ID: 38990
Software Authors: Tang, Meng; Marin, Dmitrii; Ben Ayed, Ismail; Boykov, Yuri
Description: Kernel cuts: kernel and spectral clustering meet regularization. This work bridges the gap between two popular methodologies for data partitioning: kernel clustering and regularization-based segmentation. While addressing closely related practical problems, these general methodologies may seem very different based on how they are covered in the literature. The differences may show up in motivation, formulation, and optimization, e.g. spectral relaxation versus max-flow. We explain how regularization and kernel clustering can work together and why this is useful. Our joint energy combines standard regularization, e.g. MRF potentials, and kernel clustering criteria like normalized cut. Complementarity of such terms is demonstrated in many applications using our bound optimization Kernel Cut algorithm for the joint energy (code is publicly available). While detailing combinatorial move-making, our main focus are new linear kernel and spectral bounds for kernel clustering criteria allowing their integration with any regularization objectives with existing discrete or continuous solvers.
Homepage: https://arxiv.org/abs/1506.07439
Keywords: segmentation; Markov random fields; spectral clustering; kernel methods; bound optimization
Related Software: NYU Depth; AlexNet; Graclus; PRMLT; ImageNet; GrabCut; BSDS
Cited in: 1 Document

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