Multimedia Tools and Applications

, Volume 76, Issue 6, pp 8471–8496 | Cite as

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

  • Bum-Soo Kim
  • Yang-Sae Moon
  • Jae-Gil LeeEmail author


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.


Time-series databases Data mining Boundary image matching Time-series matching Moving average transform Partial denoising 



This research, “Geospatial Big Data Management, Analysis and Service Platform Technology Development,” was supported by the MOLIT(The Ministry of Land, Infrastructure and Transport), Korea, under the national spatial information research program supervised by the KAIA(Korea Agency for Infrastructure Technology Advancement) (16NSIP-B081011-03).


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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Department of Knowledge Service EngineeringKAISTDaejeonKorea
  2. 2.Department of Computer ScienceKangwon National UniversityChuncheonKorea

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