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We present a fast video retrieval system with three novel characteristics. First, it exploits the methods of machine learning to construct automatically a hierarchy of small subsets of features that are progressively more useful for indexing. These subsets are induced by a new heuristic method called Sort-Merge feature selection, which exploits a novel combination of Fastmap for dimensionality reduction and Mahalanobis distance for likelihood determination. Second, because these induced featuredoi:10.1016/j.cviu.2003.06.003 fatcat:xs2ln4dzwvcclilwwer2bob2pe