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In this paper, we propose two techniques for near-duplicate image detection at high confidence and large scale. First, we show that entropy-based filtering eliminates ambiguous SIFT features that cause most of the false positives, and enables claiming nearduplicity with a single match of the retained high-quality features. Second, we show that graph cut can be used for query expansion with a duplicity graph computed offline to substantially improve search quality. Evaluation with web imagesdoi:10.1145/2324796.2324798 dblp:conf/mir/DongWCL12 fatcat:ox2eabongveypa2cglvy7l3jxm