Selectivity estimation for optimizing similarity query in multimedia databases

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For multimedia databases, a fuzzy query consists of a logical combination of content based similarity queries on features such as the color and the texture which are represented in continuous dimensions. Since features are intrinsically multi-dimensional, the multi-dimensional selectivity estimation is required in order to optimize a fuzzy query. The histogram is popularly used for the selectivity estimation. But the histogram has the shortcoming. It is difficult to estimate the selectivity of a similarity query, since a typical similarity query has the shape of a hyper sphere and the ranges of features are continuous. In this paper, we propose a curve fitting method using DCT to estimate the selectivity of a similarity query with a spherical shape in multimedia databases. Experiments show the effectiveness of the proposed method.
Publisher
SPRINGER-VERLAG BERLIN
Issue Date
2003
Language
English
Article Type
Article; Proceedings Paper
Citation

INTELLIGENT DATA ENGINEERING AND AUTOMATED LEARNING, v.2690, pp.638 - 644

ISSN
0302-9743
URI
http://hdl.handle.net/10203/82052
Appears in Collection
RIMS Journal Papers
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