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In this paper, we propose a probabilistic graphical model to represent weakly annotated images. We consider an image as weakly annotated if the number of keywords defined for it is less than the maximum number defined in the ground truth. This model is used to classify images and automatically extend existing annotations to new images by taking into account semantic relations between keywords. The proposed method has been evaluated in visual-textual classification and automatic annotation ofdoi:10.1109/icdar.2009.170 dblp:conf/icdar/BarratT09 fatcat:gzfh64zfbrgu5afepvtgqao7di