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Thin cloud detection for all-sky images is a challenge in ground-based sky-imaging systems because of low contrast and vague boundaries between cloud and sky regions. We treat cloud detection as a labeling problem based on the Markov random field model. In this model, each pixel is represented by a combined-feature vector that aims at improving the disparity between thin cloud and sky. The distribution of each label in the feature space is defined as a Gaussian model. Spatial information is coded by a generalized Potts model. During the estimation, thin cloud is detected by minimizing the posterior energy with an iterative procedure. Both subjective and objective evaluation results demonstrate higher accuracy of the algorithm compared with some other algorithms.