Boundary image matching supporting partial denoising using time-series matching techniques

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.
Publisher
SPRINGER
Issue Date
2017-03
Language
English
Keywords

MOVING AVERAGE TRANSFORM; OBJECT RECOGNITION; DATABASES

Citation

MULTIMEDIA TOOLS AND APPLICATIONS, v.76, no.6, pp.8471 - 8496

ISSN
1380-7501
DOI
10.1007/s11042-016-3479-y
URI
http://hdl.handle.net/10203/223603
Appears in Collection
KSE-Journal Papers(저널논문)
Files in This Item
There are no files associated with this item.
  • Hit : 147
  • Download : 0
  • Cited 0 times in thomson ci
This item is cited by other documents in WoS
⊙ Detail Information in WoSⓡClick to seewebofscience_button

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0