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JIIS
2002

Combining Approximation Techniques and Vector Quantization for Adaptable Similarity Search

8 years 6 months ago
Combining Approximation Techniques and Vector Quantization for Adaptable Similarity Search
Adaptable similarity queries based on quadratic form distance functions are widely popular in data mining application domains including 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 quadratic form distances between quantized vectors is difficult and computationally expensive. Our solution provides a variety of new approximation techniques for quantized vectors which are combined by an extended multistep query processing architecture. In our analysis section, we show that the filter steps complement each other. Consequently, it is useful to apply our filters in combination. We show the superiority of our approach over other architectures and over competitive query processing methods. In our experime...
Christian Böhm, Hans-Peter Kriegel, Thomas Se
Added 22 Dec 2010
Updated 22 Dec 2010
Type Journal
Year 2002
Where JIIS
Authors Christian Böhm, Hans-Peter Kriegel, Thomas Seidl
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