A conditional random field (CRF) model for cloud detection in ground based sky images is presented. We show that very high cloud detection accuracy can be achieved by combining a discriminative classifier and a higher order clique potential in a CRF framework. The image is first divided into homogeneous regions using a mean shift clustering algorithm and then a CRF model is defined over these regions. The various parameters involved are estimated using training data and the inference is performed using Iterated Conditional Modes (ICM) algorithm. We demonstrate how taking spatial context into account can boost the accuracy. We present qualitative and quantitative results to prove the superior performance of this framework in comparison with other state of the art methods applied for cloud detection.
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