DOI QR코드

DOI QR Code

시계열 데이터 기반의 부분 노이즈 제거 윤곽선 이미지 매칭

Partial Denoising Boundary Image Matching Based on Time-Series Data

  • 김범수 (강원대학교 정보통신연구소) ;
  • 이상훈 (강원대학교 정보통신연구소) ;
  • 문양세 (강원대학교 컴퓨터과학과)
  • 투고 : 2014.06.23
  • 심사 : 2014.09.15
  • 발행 : 2014.11.15

초록

윤곽선 이미지 매칭에서 이미지의 노이즈를 제거하는 것은 직관적이고 정확한 매칭을 위해 매우 중요한 요소이다. 본 논문에서는 윤곽선 이미지 매칭에서 부분 노이즈를 허용하는 문제를 시계열 도메인에서 다룬다. 이를 위해, 먼저 부분 노이즈 제거 시계열(partial denoising time-series)을 정의하여 이미지 도메인이 아닌 시계열 도메인에서 매칭 문제를 신속하게 해결하는 방법을 제안한다. 다음으로, 두 윤곽선 이미지, 즉 질의 시계열과 데이터 시계열에서 구성된 부분 노이즈 제거 시계열들 간에 가질 수 있는 최소거리인 부분 노이즈 제거 거리(partial denoising distance)를 제시한다. 본 논문에서는 이를 두 윤곽선 이미지 간의 유사성 척도로 사용하여 윤곽선 이미지 매칭을 수행한다. 그러나, 부분 노이즈 제거 거리를 측정하기 위해서는 매우 많은 계산이 빈번하게 발생하므로, 본 논문에서는 부분 노이즈 제거 거리의 하한을 구하는 방법을 제안한다. 마지막으로, 부분 노이즈 제거 윤곽선 이미지 매칭의 질의 방식에 따라 범위 질의 매칭과 k-NN 질의 매칭을 각각 제안한다. 실험 결과, 제안한 부분 노이즈 제거 윤곽선 이미지 매칭은 성능을 수 배에서 수십 배까지 향상시킨 것으로 나타났다.

Removing noise, called denoising, is an essential factor for the more intuitive and more accurate results in boundary image matching. This paper deals with a partial denoising problem that tries to allow a limited amount of partial noise embedded in boundary images. To solve this problem, we first define partial denoising time-series which can be generated from an original image time-series by removing a variety of partial noises and propose an efficient mechanism that quickly obtains those partial denoising time-series in the time-series domain rather than the image domain. We next present 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 we use this partial denoising distance as a similarity measure in boundary image matching. Using the partial denoising distance, however, incurs a severe computational overhead since there are a large number of partial denoising time-series to be considered. To solve this problem, we derive a tight lower bound for the partial denoising distance and formally prove its correctness. We also propose range and k-NN search algorithms exploiting the partial denoising distance in boundary image matching. Through extensive experiments, we finally show that our lower bound-based approach improves search performance by up to an order of magnitude in partial denoising-based boundary image matching.

키워드

과제정보

연구 과제 주관 기관 : 한국연구재단

참고문헌

  1. R. Agrawal, C. Faloutsos, and A. Swami, "Efficient Similarity Search in Sequence Databases," Proc. the 4th Int'l Conf. on Foundations of Data Organization and Algorithms, Chicago, Illinois, pp. 69-84, Oct. 1993.
  2. C. Faloutsos, M. Ranganathan, and Y. Manolopoulos, "Fast Subsequence Matching in Time-Series Databases," Proc. Int'l Conf. on Management of Data, ACM SIGMOD, Minneapolis, Minnesota, pp. 419-429, May 1994.
  3. M.-S. Gil, Y.-S. Moon, and B.-S. Kim, "Linear Detrending Subsequence Matching in Time-Series Databases," IEICE Trans. on Information and Systems, Vol. E94-D, No. 4, pp. 917-920, Apr. 2011. https://doi.org/10.1587/transinf.E94.D.917
  4. W.-S. Han, J. Lee, Y.-S. Moon, S.-W. Hwang, and H. Yu, "A New Approach for Processing Ranked Subsequence Matching Based on Ranked Union," Proc. Int'l Conf. on Management of Data, ACM SIGMOD, Athens, Greece, pp. 457-468, Jun. 2011.
  5. Y.-S. Moon, B.-S. Kim, M. S. Kim, and K.-Y. Whang, "Scaling-Invariant Boundary Image Matching Using Time-Series Matching Techniques," Data & Knowledge Engineering, Vol. 69, No. 10, pp. 1022-1042, Oct. 2010. https://doi.org/10.1016/j.datak.2010.07.001
  6. M. S. Kim, K.-Y. Whang, and Y.-S. Moon, "Horizontal Reduction: Instance-Level Dimensionality Reduction for Similarity Search in Large Document Databases," Proc. the 28th IEEE Int'l Conf. on Data Engineering(ICDE), Washington, DC, pp. 1061-1072, Apr. 2012.
  7. B.-S. Kim, Y.-S. Moon, M.-J. Choi, and J. Kim, "Interactive Noise-Controlled Boundary Image Matching Using the Time-Series Moving Average Transform," Multimedia Tools and Applications, Jun. 2013 (published online).
  8. H. Liu, G. Frishkoff, R. Frank, and D. Dou, "Sharing and Integration of Cognitive Neuroscience Data: Metric and Pattern Matching across Heterogeneous ERP Datasets," Neurocomputing, Vol. 92, pp. 156-169, Sept. 2012. https://doi.org/10.1016/j.neucom.2012.01.028
  9. M. Vlachos, Z. Vagena, P. S. Yu, and V. Athitsos, "Rotation Invariant Indexing of Shapes and Line Drawings," Proc. of ACM Conf. on Information and Knowledge Management, Bremen, Germany, pp. 131-138, Oct. 2005.
  10. Y.-S. Moon and J. Kim, "Efficient Moving Average Transform-Based Subsequence Matching Algorithms in Time-Series Databases," Information Sciences, Vol. 177, No. 23, pp. 5415-5431, Dec. 2007. https://doi.org/10.1016/j.ins.2007.05.038
  11. Y.-S. Moon, K.-Y. Whang, and W.-S. Han, "General Match: A Subsequence Matching Method in Time- Series Databases Based on Generalized Windows," Proc. Int'l Conf. on Management of Data, ACM SIGMOD, Madison, Wisconsin, pp. 382-393, Jun. 2002.
  12. W.-S. Han, J. Lee, Y.-S. Moon, and H. Jiang, "Ranked Subsequence Matching in Time-Series Databases," Proc. the 33rd Int'l Conf. on Very Large Data Bases, Vienna, Austria, pp. 423-434, Sept. 2007.
  13. M. Vlachos, G. Kollios, and D. Gunopulos, "Discovering Similar Multidimensional Trajectories," Proc. the 18th IEEE Int'l Conf. on Data Engineering( ICDE), San Jose, California, pp. 673-684, Feb./Mar. 2002.
  14. R. H. Shumway and D. S. Stoffer, Time Series Analysis and Its Applications: With R Examples(Ed. 2), Springer Texts in Statistics, 2006.
  15. K. W. Chu and M. H. Wong, "Fast Time-Series Searching with Scaling and Shifting," Proc. ACM SIGACT-SIGMOD-SIGART Symp. on Principles of Database Systems, Philadelphia, Pennsylvania, pp. 237-248, May 1999.
  16. D. Rafiei and A. O. Mendelzon, "Querying Time Series Data Based on Similarity," IEEE Trans. on Knowledge and Data Engineering, Vol. 12, No. 5, pp. 675-693, Sept./Oct. 2000. https://doi.org/10.1109/69.877502
  17. W.-K. Loh, S.-W. Kim, and K.-Y. Whang, "A Subsequence Matching Algorithm that Supports Normalization Transform in Time-Series Databases," Data Mining and Knowledge Discovery, Vol. 9, No. 1, pp. 5-28, Jul. 2004. https://doi.org/10.1023/B:DAMI.0000026902.89522.a3
  18. W. K. Pratt, Digital Image Processing, 4th Ed., Eastman Kodak Company, Rochester, New York, 2007.
  19. C.-H. Lin and W.-C. Lin, "Image Retrieval System Based on Adaptive Color Histogram and Texture Features," The Computer Journal, Vol. 54, No. 7, pp. 1136-1147, Jul. 2011. https://doi.org/10.1093/comjnl/bxq066
  20. M. N. Do, "Wavelet-Based Texture Retrieval Using Generalized Gaussian Density and Kullback-Leibler Distance," IEEE Trans. on Image Processing, Vol. 11, No. 2, pp. 146-158, Feb. 2002. https://doi.org/10.1109/83.982822
  21. X.-Y. Wang, Y.-J. Yu, and H.-Y. Yang, "An Effective Image Retrieval Scheme Using Color, Texture and Shape Features," Computer Standards & Interfaces, Vol. 33, No. 1, pp. 59-68, Jan. 2011. https://doi.org/10.1016/j.csi.2010.03.004
  22. P. Suetens, P. Fua, and A. J. Hanson, "Computational Strategies for Object Recognition," ACM Computing Surveys, Vol. 24, No. 1, pp. 5-62, Mar. 1992. https://doi.org/10.1145/128762.128763
  23. D. Z. Zhang and G. Lu, "Review of Shape Representation and Description Techniques," Pattern Recognition, Vol. 37, No. 1, pp. 1-19, Jul. 2003.
  24. R. C. Gonzalez and R. E. Woods, Digital Image Processing, 3rd Ed., Prentice Hall, New Jersey, 2008.
  25. S. Belongie and J. Malik, "Matching with Shape Contexts," Proc. IEEE Workshop on Content based Access of Image and Video Libraries (CBAIVL- 2000), Hilton Head Island, South Carolina, pp. 20-26, Jun. 2000.
  26. M. B. Holte, T. B. Moeslund, and P. Fihl, "View-Invariant Gesture Recognition using 3D Optical Flow and Harmonic Motion Context," Computer Vision and Image Understanding, Vol. 114, No. 12, pp. 1353-1361, Dec. 2010. https://doi.org/10.1016/j.cviu.2010.07.012
  27. G. Mori and J. Malik, "Estimating Human Body Configuration Using Shape Context Matching," Proc. the 7th European Conference on Computer Vision, Copenhagen, Denmark, pp. 666-680, May 2002.
  28. Yefeng Zheng, http://www.umiacs.umd.edu/zhengyf/PointMatching.htm