Segmentation Based on Improved Markov Chain Monte Carlo
Xiangrong Wang · International Conference on Electronics, Communications and Control · 2012
To overcome the drawback of Simulated Annealing (SA), a novel algorithm Using Population based Markov chain Monte Carlo (Pop-MCMC) is proposed for segmentation. It takes less time from the initial state to the state that chains are coupling with Pop-MCMC than with the Simulated Annealing which is usually employed in traditional MCMC. The main feature Pop-MCMC owns is that multiple samples are generated at a time and information is exchanged between the Markov Chains. A graph is constructed with the atomic regions which are formed using the Mean Shift filter. Secondly, the Swendsen-Wang Cuts Algorithm is employed to construct the Markov chain based on the reconstructed energy function. Thirdly, pop-MCMC is employed to speed up the convergence of the Markov chain. Experiments show our algorithm achieves better segmentation results.