Abstract
To support fast k-NN queries over large image database, in this paper, we propose a novel dual distance transformation method called DDT. In DDT, all images are first grouped into clusters by the k-Means clustering algorithm. Then the start- and centroid-distances of each image are combined to obtain the uniform index key through its dual distance transformation. Finally the keys are indexed by a B + -tree. Thus, given a query image, its k-nearest neighbor query in high-dimensional space is transformed into a search in a single dimensional space with the aid of the DDT index. Extensive performance studies are conducted to evaluate the effectiveness and efficiency of the proposed scheme. Our results demonstrate that this method outperforms the state-of-the-art high dimensional search techniques, such as the X-Tree, VA-file, iDistance and NB-Tree.
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© 2006 Springer-Verlag Berlin Heidelberg
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Zhuang, Y., Wu, F. (2006). Fast Answering k-Nearest-Neighbor Queries over Large Image Databases Using Dual Distance Transformation. In: Cham, TJ., Cai, J., Dorai, C., Rajan, D., Chua, TS., Chia, LT. (eds) Advances in Multimedia Modeling. MMM 2007. Lecture Notes in Computer Science, vol 4351. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69423-6_40
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DOI: https://doi.org/10.1007/978-3-540-69423-6_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69421-2
Online ISBN: 978-3-540-69423-6
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