Abstract
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.
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Lee, JH., Chun, SJ., Park, S. (2003). Selectivity Estimation for Optimizing Similarity Query in Multimedia Databases. In: Liu, J., Cheung, Ym., Yin, H. (eds) Intelligent Data Engineering and Automated Learning. IDEAL 2003. Lecture Notes in Computer Science, vol 2690. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45080-1_86
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DOI: https://doi.org/10.1007/978-3-540-45080-1_86
Publisher Name: Springer, Berlin, Heidelberg
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