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Adaptable Similarity Search Using Vector Quantization
[chapter]
2001
Lecture Notes in Computer Science
Adaptable similarity queries based on quadratic form distance functions are widely popular in data mining applications, particularly for domains such as multimedia, CAD, molecular biology or medical image databases. Recently it has been recognized that quantization of feature vectors can substantially improve query processing for Euclidean distance functions, as demonstrated by the scan-based VA-file and the index structure IQ-tree. In this paper, we address the problem that determining
doi:10.1007/3-540-44801-2_31
fatcat:jcgrx26ymbgv7luvxtc4vzxwxq