Contextual Image Segmentation based on AdaBoost and Markov Random Fields

AdaBoost, one of machine learning algorithms, is employed for classification of land-cover categories of geostatistical data. We assume that the posterior probability is given by the odds ratio due to loss functions. Further, landcover categories are assumed to follow Markov random fields (MRF). Then, we derive a classifier by combining two posteriors based on AdaBoost and MRF through the iterative conditional modes. Our procedure is applied to benchmark data sets provided by IEEE GRSS Data Fusion Committee and shows an excellent performance.