Gibbs sampler and maximum likelihood estimation for unsupervised image segmentations

In this paper, we propose a new way of obtaining an unsupervised image segmentation. Similar to the previous unsupervised image segmentation algorithms, the proposed algorithm is iterative. However, we adopt a different stopping rule such that if the region label at each pixel does not change for a sufficient number of iterations, then we confirm it as a final one and never visit the corresponding pixel for the update thereafter. Eventually, all pixels in the image will be confirmed to assure the convergence. Another feature of the proposed algorithm is that the Gibbs sampler is adopted to generate a region label for the update. This probabilistic region label update will resolve the problem of sticking at local maximum too early. Adding all these features together, the proposed algorithm updates region labels with Gibbs sampler using the current model parameter values and then, using the current region labels, the maximum likelihood estimates of the model parameters are obtained. This alternate procedure continues until all pixels in the image are declared as the confirmed final labels. Experimental results on the synthesized images with independent Gaussian noise show that the proposed algorithm yields significantly lower error rates than the previous deterministic unsupervised segmentation algorithms.