DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Bum-Soo | ko |
dc.contributor.author | Moon, Yang-Sae | ko |
dc.contributor.author | Lee, Jae-Gil | ko |
dc.date.accessioned | 2017-05-10T04:15:51Z | - |
dc.date.available | 2017-05-10T04:15:51Z | - |
dc.date.created | 2016-05-24 | - |
dc.date.created | 2016-05-24 | - |
dc.date.created | 2016-05-24 | - |
dc.date.issued | 2017-03 | - |
dc.identifier.citation | MULTIMEDIA TOOLS AND APPLICATIONS, v.76, no.6, pp.8471 - 8496 | - |
dc.identifier.issn | 1380-7501 | - |
dc.identifier.uri | http://hdl.handle.net/10203/223603 | - |
dc.description.abstract | In this paper, we deal with the problem of boundary image matching which finds similar boundary images regardless of partial noise exploiting time-series matching techniques. Time-seris matching techniques make it easier to compute distances for similarity identification, and therefore it is feasible to perform boundary image matching even on a large image database. To solve this problem, we first convert all boundary images into times-series and derive partial denoising time-series. The partial denoising time-series is generated from an original time-series by removing partial noise; that is, it is obtained by changing a position of partial denoising from original time-series. We then introduce the partial denoising distance, which is the minimum distance from a query time-series to all possible partial denoising time-series generated from a data time-series, and propose partial denoising boundary image matching using the partial denoising distance as a similarity measure. Computing the partial denoising distance, however, incurs a severe computational overhead since there are a large number of partial denoising time-series to be considered. Thus, in order to improve its performance, we present a tight lower bound of the partial denoising distance and also optimize the computation of the partial denoising distance. We finally propose range and k-NN query algorithms according to a query processing method for partial denoising boundary image matching. Through extensive experiments, we show that our lower bound-based approach and the optimization method of the partial denoising distance improve search performance by up to an order of magnitude. | - |
dc.language | English | - |
dc.publisher | SPRINGER | - |
dc.title | Boundary image matching supporting partial denoising using time-series matching techniques | - |
dc.type | Article | - |
dc.identifier.wosid | 000399017800038 | - |
dc.identifier.scopusid | 2-s2.0-84962746755 | - |
dc.type.rims | ART | - |
dc.citation.volume | 76 | - |
dc.citation.issue | 6 | - |
dc.citation.beginningpage | 8471 | - |
dc.citation.endingpage | 8496 | - |
dc.citation.publicationname | MULTIMEDIA TOOLS AND APPLICATIONS | - |
dc.identifier.doi | 10.1007/s11042-016-3479-y | - |
dc.contributor.localauthor | Lee, Jae-Gil | - |
dc.contributor.nonIdAuthor | Kim, Bum-Soo | - |
dc.contributor.nonIdAuthor | Moon, Yang-Sae | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | Time-series databases | - |
dc.subject.keywordAuthor | Data mining | - |
dc.subject.keywordAuthor | Boundary image matching | - |
dc.subject.keywordAuthor | Time-series matching | - |
dc.subject.keywordAuthor | Moving average transform | - |
dc.subject.keywordAuthor | Partial denoising | - |
dc.subject.keywordPlus | MOVING AVERAGE TRANSFORM | - |
dc.subject.keywordPlus | OBJECT RECOGNITION | - |
dc.subject.keywordPlus | DATABASES | - |
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