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Modelling spatial information of local features is known to improve performance in image categorisation. Compared with simple pairwise features and visual phrases, graphs can capture the structural organisation of local features more adequately. Besides, a dense regular grid can guarantee a more reliable representation than the interest points and give better results for image classification. In this study, the authors introduced a bag of dense local graphs approach that combines the performance of bag of visual words expressing the image classification process with the representational power of graphs. The images were represented with dense local graphs built upon dense scale-invariant feature transform descriptors. The graph-based substructure pattern mining algorithm was applied on the local graphs to discover the frequent local subgraphs, producing a bag of subgraphs representation. The results were reported from experiments conducted on four challenging benchmarks. The findings show that the proposed subgraph histogram improves the categorisation accuracy.