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MCMC based sampling technique for robust multi-model fitting and visual data segmentation

This paper approaches the problem of geometric multi-model fitting as a data segmentation problem which is solved by a sequence of sampling, model selection and clustering steps. We propose a sampling method that significantly facilitates solving the segmentation problem using the Normalized cut. The sampler is a novel application of Markov-Chain-Monte-Carlo (MCMC) method to sample from a distribution in the parameter space that is obtained by modifying the Least kth Order Statistics cost function. To sample from this distribution effectively, our proposed Markov Chain includes novel long and short jumps to ensure exploration and exploitation of all structures. It also includes fast local optimization steps to target all, even fairly small, putative structures. This leads to a clustering solution through which final model parameters for each segment are obtained. The method competes favorably with the state-of-the-art both in terms of computation power and segmentation accuracy.