Simulated annealing and iterated conditional modes with selective and confidence enhanced update schemes

摘要

Proposes a selective update scheme for both SA (simulated annealing) and ICMs (iterated conditional modes) which only visits sites within inhomogeneous neighborhoods. A second scheme is proposed to enhance the update confidence at each site by incorporating contextual information in terms of neighbor label class probability distributions, instead of their current realizations. The two update schemes reduce the computation demand and improve estimation accuracy. Both schemes are tested on a noise-contaminated Markov random field test image. The results show that ICM and SA with selective update achieve a computational savings of five times on average, without introducing noticeable degradation. The confidence enhanced update scheme, working with SA and ICM, much improves the final estimation accuracy. In particular for ICM, it produces similar results to those of SA, but uses only a fraction of the iterations needed by the latter.