Abstract
In the past, researchers tried hard classification techniques with contextual information to improve classification results. While modelling the spatial contextual information for hard classifiers using Markov Random Field it has been found that the Metropolis algorithm is easier to program and it performs better when compared with the Gibbs sampler. In this study, it has been found that in the case of soft contextual classification, the Metropolis algorithm fails to sample from a random field efficiently and the Gibbs sampler performs better than the Metropolis algorithm, due to the high dimensionality of the soft classification outputs.
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