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Kernel Clustering meets Markov Random Fields


Yuri Boykov
Western University

May 19, 2017 at  11:00 AM
McConnell Engineering Room 437

Please note there will be two back to back talks for 45 minutes each, starting at 11am
Abstract:

The talk starts with an overview of standard kernel clustering techniques and their limitations. I particular we prove "Breiman's bias" under certain conditions and discuss its solutions. The talk also presents a new segmentation model combining common regularization energies, e.g. Markov Random Field (MRF) potentials, with standard pairwise clustering criteria like Normalized Cut (NC), average association (AA), etc. These clustering and regularization models are widely used in machine learning and computer vision, but they were not combined before due to significant differences in the corresponding optimization, e.g. spectral relaxation and combinatorial max-flow techniques. On the one hand, we show that common applications using MRF segmentation energies can benefit from a high-order NC term, e.g. enforcing balanced clustering of arbitrary high-dimensional image features combining color, texture, location, depth, motion, etc. On the other hand, standard clustering applications can benefit from an inclusion of common pairwise or higher-order MRF constraints, e.g. edge alignment, bin-consistency, label cost, etc. To address joint energies like NC+MRF, we propose efficient Kernel Cut algorithms based on bound optimization.