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

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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.

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Notes

  1. 1 According to the report of [5], the accuracy differences among different similarity measures are not significant. Thus, even though we use other similarity measures including the dynamic time warping distance, the matching result will be very similar with the case of using the Euclidean distance in this paper. Also, in this paper we focus on the performance improvement, i.e., the reduction of execution times, rather than the accuracy improvement of removing the partial noise. To confirm the performance improvement, we adopt and optimize the Euclidean distance, which is simple and one of the most widely used distance measures. For the other similarity measures, we need to develop different optimization techniques, which are out of scope of this paper.

  2. 2 The entry values used by the moving average transform may be included in the other values besides the values corresponding to the partial noise; that is, the moving average transform may interchangeably use not only the partial noise values but also the other values of the boundary time-series for removing noise. Although the partial denoising is affected by the entry values, we especially do not consider the effect since it is insignificant.

  3. 3 http://www.umiacs.umd.edu/~zhengyf/PointMatching.htm

References

  1. Agrawal R, Faloutsos C, Swami A (1993) Efficient similarity search in sequence databases. In: Proceedings of the 4th Int’l Conf. on Foundations of Data Organization and Algorithms, Chicago, Illinois, pp 69–84

  2. Belongie S, Malik J, Puzicha J (2002) Shape matching and object recognition using shape contexts. IEEE Trans. on Pattern Analysis and Machine Intelligence 24 (4):509–522

    Article  Google Scholar 

  3. Chu KW, Wong MH (1999) Fast time-series searching with scaling and shifting. In: Proceedings of the ACM SIGACT-SIGMOD-SIGART Symp. on Principles of Database Systems, Philadelphia, Pennsylvania, pp 237–248

  4. Do MN (2002) Wavelet-based texture retrieval using generalized gaussian density and Kullback-Leibler distance. IEEE Trans Image Processing 11(2):146–158

    Article  MathSciNet  Google Scholar 

  5. Ding H, Trajcevski G, Scheuermann P, Wang X, Keogh E (2008) Querying and mining of time series data: experimental comparison of representations and distance measures. In: Proceedings of the 34th Int’l Conf. on Very Large Data Bases, Auckland, New Zealand, pp 1542– 1552

  6. Faloutsos C, Ranganathan M, Manolopoulos Y (1994) Fast subsequence matching in time-series databases. In: Proceedings of the Int’l Conf. on Management of Data, ACM SIGMOD, Minneapolis, Minnesota, pp 419–429

  7. Gil M-S, Moon Y-S, Kim B-S (2011) Linear detrending subsequence matching in time-series databases. IEICE Trans Information and Systems E94-D(4):917–920

    Article  Google Scholar 

  8. Gonzalez RC, Woods RE (2008) Digital image processing, 3rd Edn. Prentice Hall, New Jersey

    Google Scholar 

  9. Han W-S, Lee J, Moon Y-S, Hwang S-W, Yu H (2011) A new approach for processing ranked subsequence matching based on ranked union. In: Proceedings of Int’l Conf. on Management of Data, ACM SIGMOD, Athens, Greece, pp 457–468

  10. Holte MB, Moeslund TB, Fihl P (2010) View-invariant gesture recognition using 3D optical flow and harmonic motion context. Comput Vis Image Underst 114 (12):1353–1361

    Article  Google Scholar 

  11. Kim B-S, Moon Y-S, Choi M-J, Kim J (2014) Interactive noise-controlled boundary image matching using the time-series moving average transform. Multimedia Tools and Applications 72(3):2543– 2571

    Article  Google Scholar 

  12. Lin C-H, Lin W-C (2011) Image retrieval system based on adaptive color histogram and texture features. Comput J 54(7):1136–1147

    Article  Google Scholar 

  13. Liu H, Frishkoff G, Frank R, Dou D (2012) Sharing and integration of cognitive neuroscience data: Metric and pattern matching across heterogeneous ERP datasets. Neurocomputing 92:156– 169

    Article  Google Scholar 

  14. Loh W-K, Kim S-W, Whang K-Y (2004) A subsequence matching algorithm that supports normalization transform in time-series databases. Data Min Knowl Disc 9(1):5–28

    Article  MathSciNet  Google Scholar 

  15. Moon Y-S, Whang K-Y, Han W-S (2002) General match: a subsequence matching method in time-series databases based on generalized windows. In: proceedings of the Int’l Conf. on Management of Data, ACM SIGMOD, Madison, Wisconsin, pp 382–393

  16. Moon Y-S, Kim J (2007) Efficient moving average transform-based subsequence matching algorithms in time-series databases. Inf Sci 177(23):5415–5431

    Article  MATH  Google Scholar 

  17. Moon Y-S, Kim B-S, Kim MS, Whang K-Y (2010) Scaling-invariant boundary image matching using time-series matching techniques. Data Knowl Eng 69 (10):1022–1042

    Article  Google Scholar 

  18. Moon Y-S, Lee BS (2014) Safe MBR-transformation in similar sequence matching. Inf Sci 270(2):28–40

    Article  MathSciNet  MATH  Google Scholar 

  19. Mori G, Malik J (2002) Estimating human body configuration using shape context matching. In: Proceedings of the 7th European Conf. on Computer Vision, Copenhagen, Denmark, pp 666– 680

  20. Pratt WK (2007) Digital image processing, 4th Edn. Eastman Kodak Company, Rochester

    Book  MATH  Google Scholar 

  21. Rafiei D, Mendelzon AO (2000) Querying time series data based on similarity. IEEE Trans Knowl Data Eng 12(5):675–693

    Article  Google Scholar 

  22. Suetens P, Fua P, Hanson AJ (1992) Computational strategies for object recognition. ACM Comput Surv 24(1):5–62

    Article  Google Scholar 

  23. Shumway RH, Stoffer DS (2006) Time series analysis and its applications: with r examples(Ed. 2) springer texts in statistics

  24. Vlachos M, Kollios G, Gunopulos D (2002) Discovering similar multidimensional trajectories. In: Proceedings of the 18th IEEE Int’l Conf. on Data Engineering(ICDE), San Jose, California, pp 673– 684

  25. Vlachos M, Vagena Z, Yu PS, Athitsos V (2005) Rotation invariant indexing of shapes and line drawings. In: Proceedings of ACM Conf. on Information and Knowledge Management, Bremen, Germany, pp 131–138

  26. Wang X-Y, Yu Y-J, Yang H-Y (2011) An effective image retrieval scheme using color, texture and shape features. Computer Standards & Interfaces 33(1):59–68

    Article  Google Scholar 

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Acknowledgments

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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Authors and Affiliations

  1. Department of Knowledge Service Engineering, KAIST, Daejeon, Korea

    Bum-Soo Kim & Jae-Gil Lee

  2. Department of Computer Science, Kangwon National University, Chuncheon, Korea

    Yang-Sae Moon

Authors
  1. Bum-Soo Kim
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  2. Yang-Sae Moon
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  3. Jae-Gil Lee
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Corresponding author

Correspondence to Jae-Gil Lee.

Additional information

The preliminary version of this paper was published in Proc. of the Int’l Conf. on Big Data and Smart Computing (BigComp 2015), Jeju, Korea, pp. 136–141, Feb. 2015.

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Kim, BS., Moon, YS. & Lee, JG. Boundary image matching supporting partial denoising using time-series matching techniques. Multimed Tools Appl 76, 8471–8496 (2017). https://doi.org/10.1007/s11042-016-3479-y

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