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• W2107568013 abstract "We propose a new dynamic index structure called the GC-tree (or the grid cell tree) for efficient similarity search in image databases. The GC-tree is based on a special subspace partitioning strategy which is optimized for a clustered high-dimensional image dataset. The basic ideas are threefold: 1) we adaptively partition the data space based on a density function that identifies dense and sparse regions in a data space; 2) we concentrate the partition on the dense regions, and the objects in the sparse regions of a certain partition level are treated as if they lie within a single region; and 3) we dynamically construct an index structure that corresponds to the space partition hierarchy. The resultant index structure adapts well to the strongly clustered distribution of high-dimensional image datasets. To demonstrate the practical effectiveness of the GC-tree, we experimentally compared the GC-tree with the IQ-tree, LPC-file, VA-file, and linear scan. The result of our experiments shows that the GC-tree outperforms all other methods." @default.
• W2107568013 created "2016-06-24" @default.
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• W2107568013 date "2002-06-01" @default.
• W2107568013 modified "2023-10-16" @default.
• W2107568013 title "The GC-tree: a high-dimensional index structure for similarity search in image databases" @default.
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• W2107568013 doi "https://doi.org/10.1109/tmm.2002.1017736" @default.
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