DC Field | Value | Language |
---|---|---|
dc.contributor.author | Choi, Sugil | ko |
dc.contributor.author | Lee, Suwon | ko |
dc.contributor.author | Yang, Hyun-Seung | ko |
dc.date.accessioned | 2016-04-20T06:53:15Z | - |
dc.date.available | 2016-04-20T06:53:15Z | - |
dc.date.created | 2015-11-18 | - |
dc.date.created | 2015-11-18 | - |
dc.date.issued | 2015-11 | - |
dc.identifier.citation | ELECTRONICS LETTERS, v.51, no.24, pp.1992 - 1993 | - |
dc.identifier.issn | 0013-5194 | - |
dc.identifier.uri | http://hdl.handle.net/10203/205553 | - |
dc.description.abstract | Binary codes play an important role in many computer vision applications. They require less storage space while allowing efficient computations. However, a linear search to find the best matches among binary data creates a bottleneck for large-scale datasets. Among the approximation methods used to solve this problem, the hierarchical clustering tree (HCT) method is a state-of the-art method. However, the HCT performs a hard assignment of each data point to only one cluster, which leads to a quantisation error and degrades the search performance. As a solution to this problem, an algorithm to create hierarchical soft clustering tree (HSCT) by assigning a data point to multiple nearby clusters in the Hamming space is proposed. Through experiments, the HSCT is shown to outperform other existing methods. | - |
dc.language | English | - |
dc.publisher | INST ENGINEERING TECHNOLOGY-IET | - |
dc.title | Hierarchical soft clustering tree for fast approximate search of binary codes | - |
dc.type | Article | - |
dc.identifier.wosid | 000365573200020 | - |
dc.identifier.scopusid | 2-s2.0-84948409184 | - |
dc.type.rims | ART | - |
dc.citation.volume | 51 | - |
dc.citation.issue | 24 | - |
dc.citation.beginningpage | 1992 | - |
dc.citation.endingpage | 1993 | - |
dc.citation.publicationname | ELECTRONICS LETTERS | - |
dc.identifier.doi | 10.1049/el.2015.2806 | - |
dc.contributor.localauthor | Yang, Hyun-Seung | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | binary codes | - |
dc.subject.keywordAuthor | Hamming codes | - |
dc.subject.keywordAuthor | search problems | - |
dc.subject.keywordAuthor | computer vision | - |
dc.subject.keywordAuthor | image coding | - |
dc.subject.keywordAuthor | approximation theory | - |
dc.subject.keywordAuthor | quantisation (signal) | - |
dc.subject.keywordAuthor | pattern clustering | - |
dc.subject.keywordAuthor | tree codes | - |
dc.subject.keywordAuthor | hierarchical soft clustering tree | - |
dc.subject.keywordAuthor | approximate search | - |
dc.subject.keywordAuthor | binary code | - |
dc.subject.keywordAuthor | computer vision | - |
dc.subject.keywordAuthor | linear search | - |
dc.subject.keywordAuthor | approximation method | - |
dc.subject.keywordAuthor | HSCT method | - |
dc.subject.keywordAuthor | quantisation error | - |
dc.subject.keywordAuthor | data point assignment | - |
dc.subject.keywordAuthor | Hamming space | - |
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