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We describe an image representation that combines the representational power of graphs with the efficiency of the bag-of-words model. For each image in a data set, first, a graph is constructed from local patches of interest regions and their spatial arrangements. Then, each graph is represented with a histogram of sub graphs selected using a frequent subgraph mining algorithm in the whole data. Using the sub graphs as the visual words of the bag-of-words model and transforming of the graphs into a vector space using this model enables statistical classification of images using support vector machines. Experiments using images cut from a large satellite scene show the effectiveness of the proposed representation in classification of complex types of scenes into eight high-level semantic classes.