The GC-tree: A high-dimensional index structure for similarity search in image databases
DC Field | Value | Language |
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dc.contributor.author | Cha, GH | - |
dc.contributor.author | Chung, CW | - |
dc.date.available | 2021-02-22T16:31:48Z | - |
dc.date.issued | 2002-06 | - |
dc.identifier.issn | 1520-9210 | - |
dc.identifier.issn | 1941-0077 | - |
dc.identifier.uri | https://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/16412 | - |
dc.description.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. | - |
dc.format.extent | 13 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | The GC-tree: A high-dimensional index structure for similarity search in image databases | - |
dc.type | Article | - |
dc.publisher.location | 미국 | - |
dc.identifier.doi | 10.1109/TMM.2002.1017736 | - |
dc.identifier.scopusid | 2-s2.0-0036613685 | - |
dc.identifier.wosid | 000177000800008 | - |
dc.identifier.bibliographicCitation | IEEE TRANSACTIONS ON MULTIMEDIA, v.4, no.2, pp 235 - 247 | - |
dc.citation.title | IEEE TRANSACTIONS ON MULTIMEDIA | - |
dc.citation.volume | 4 | - |
dc.citation.number | 2 | - |
dc.citation.startPage | 235 | - |
dc.citation.endPage | 247 | - |
dc.type.docType | Article | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Telecommunications | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Software Engineering | - |
dc.relation.journalWebOfScienceCategory | Telecommunications | - |
dc.subject.keywordPlus | NEIGHBOR | - |
dc.subject.keywordPlus | SPACE | - |
dc.subject.keywordAuthor | dynamic index structure | - |
dc.subject.keywordAuthor | GC-tree | - |
dc.subject.keywordAuthor | high-dimensional indexing | - |
dc.subject.keywordAuthor | image database | - |
dc.subject.keywordAuthor | nearest neighbor search (NN search) | - |
dc.subject.keywordAuthor | similarity search | - |
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