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The authors consider approaches for exact similarity search in a high dimensional space of correlated features representing image datasets, based on principles of clustering and vector quantization. They develop an adaptive cluster distance bound based on separating hyper-planes that complements their index in selectively retrieving clusters that contain data entries closest to the query. This bound enables efficient spatial filtering, with a relatively small pre-processing storage overhead and is applicable to Euclidean and Mahalanobis similarity measures.
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