The GC-tree: A high-dimensional index structure for similarity search in image databases
- Authors
- Cha, GH; Chung, CW
- Issue Date
- Jun-2002
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Keywords
- dynamic index structure; GC-tree; high-dimensional indexing; image database; nearest neighbor search (NN search); similarity search
- Citation
- IEEE TRANSACTIONS ON MULTIMEDIA, v.4, no.2, pp 235 - 247
- Pages
- 13
- Journal Title
- IEEE TRANSACTIONS ON MULTIMEDIA
- Volume
- 4
- Number
- 2
- Start Page
- 235
- End Page
- 247
- URI
- https://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/16412
- DOI
- 10.1109/TMM.2002.1017736
- ISSN
- 1520-9210
1941-0077
- Abstract
- With the proliferation of multimedia data, there is an increasing need to support the indexing and retrieval of high-dimensional image data. In this paper, 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, the LPC-file, the VA-file, and the linear scan. The result of our experiments shows that the GC-tree outperforms all other methods.
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Collections - 공과대학 > 소프트웨어학부 > 1. Journal Articles
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