  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.
