KIPS Transactions on Software and Data Engineering (정보처리학회논문지:소프트웨어 및 데이터공학)

Korea Information Processing Society (한국정보처리학회)
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Symmetric-Invariant Boundary Image Matching Based on Time-Series Data

시계열 데이터 기반의 대칭-불변 윤곽선 이미지 매칭

  • 이상훈 (강원대학교 컴퓨터과학과) ;
  • 방준상 (강원대학교 컴퓨터과학과) ;
  • 문성우 (강원대학교 컴퓨터과학과) ;
  • 문양세 (강원대학교 컴퓨터과학과)
  • Received : 2015.07.06
  • Accepted : 2015.08.04
  • Published : 2015.10.31
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Abstract

In this paper we address the symmetric-invariant problem in boundary image matching. Supporting symmetric transformation is an important factor in boundary image matching to get more intuitive and more accurate matching results. However, the previous boundary image matching handled rotation transformation only without considering symmetric transformation. In this paper, we propose symmetric-invariant boundary image matching which supports the symmetric transformation as well as the rotation transformation. For this, we define the concept of image symmetry and formally prove that rotation-invariant matching of using a symmetric image always returns the same result for every symmetric angle. For efficient symmetric transformation, we also present how to efficiently extract the symmetric time-series from an image boundary. Finally, we formally prove that our symmetric-invariant matching produces the same result for two approaches: one is using the time-series extracted from the symmetric image; another is using the time-series directly obtained from the original image time-series by symmetric transformation. Experimental results show that the proposed symmetric-invariant boundary image matching obtains more accurate and intuitive results than the previous rotation-invariant boundary image matching. These results mean that our symmetric-invariant solution is an excellent approach that solves the image symmetry problem in time-series domain.

본 논문에서는 대칭 변환을 지원하는 윤곽선 이미지 매칭 문제를 다룬다. 이미지 매칭에서 이미지의 대칭 변환을 지원하는 것은 직관적이고 정확한 매칭을 위한 매우 중요한 요소이다. 그러나 기존 이미지 매칭에서는 이미지의 회전 변환만 고려하였을 뿐 대칭 변환은 고려하지 않았다. 본 논문에서는 기존 회전-불변 윤곽선 이미지 매칭에 대칭 변환까지 지원하는 대칭-불변 윤곽선 이미지 매칭을 제안한다. 이를 위해, 먼저 이미지 대칭의 개념을 정의하고, 어떠한 대칭각을 사용하더라도 회전-불변 매칭의 결과는 동일함을 정형적으로 증명한다. 또한, 대칭 변환을 위해 이미지 윤곽선으로부터 대칭 시계열을 효율적으로 추출하는 방법을 제안한다. 그런 다음, 이미지를 대칭하여 생성한 대칭 시계열과 원본 이미지 시계열을 직접 대칭하여 생성한 대칭 시계열을 사용한 회전-불변 매칭 결과가 동일함을 정형적으로 증명한다. 실험 결과, 제안하는 대칭-불변 윤곽선 이미지 매칭은 회전 변환만을 지원하는 기존 이미지 매칭에 비해 보다 정확하고 직관적인 결과를 도출하는 것으로 나타났다. 이같은 결과는 대칭-불변 윤곽선 이미지 매칭이 이미지의 대칭 변환 문제를 시계열 도메인에서 해결한 우수한 해결책임을 의미한다.

Keywords

References

  1. G. Navarro, "Spaces, Trees, and Colors: The Algorithmic Landscape of Document Retrieval on Sequences," ACM Computing Surveys, Vol.46, No.4, Article 52, Mar., 2014.
  2. J. Kumar, P. Ye, and D. Doermann, "Structural Similarity for Document Image Classification and Retrieval," Pattern Recognition Letters, Vol.43, pp.119-126, July, 2014. https://doi.org/10.1016/j.patrec.2013.10.030
  3. P. B. Patil and M. B. Kokare, "Interactive Semantic Image Retrieval," Journal of Information Processing Systems, Vol.9, No.3, pp.349-364, Sept., 2013. https://doi.org/10.3745/JIPS.2013.9.3.349
  4. Z. Xu, K. Cheng, Y. Ding, Z. Tian, and H. Zhao, "A Multiple Genome Sequence Matching Based on Skipping Tree," Int'l Journal of Machine Learning and Computing, Vol.5, No.1, pp.78-85, Feb., 2015. https://doi.org/10.7763/IJMLC.2015.V5.487
  5. R. Agrawal, C. Faloutsos, and A. Swami, "Efficient Similarity Search in Sequence Databases," in Proc. of the 4th Int'l Conf. on Foundations of Data Organization and Algorithms, Chicago, Illinois, pp.69-84, Oct., 1993.
  6. Y.-S. Moon, K.-Y. Whang, and W.-S. Han, "General Match: A Subsequence Matching Method in Time-Series Databases Based on Generalized Windows," in Proc. of Int'l Conf. on Management of Data, ACM SIGMOD, Madison, Wisconsin, pp.382-393, June, 2002.
  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, Vol.72, No.3, pp.2543-2571, Oct., 2014. https://doi.org/10.1007/s11042-013-1552-3
  8. J. Han, M. Kamber, and J. Pei, "Data Mining: Concepts and Techniques," 3rd Ed., Morgan Kaufmann, 2011.
  9. 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
  10. M. Vlachos, Z. Vagena, P. S. Yu, and V. Athitsos, "Rotation Invariant Indexing of Shapes and Line Drawings," in Proc. of ACM Conf. on Information and Knowledge Management, Bremen, Germany, pp.131-138, Oct. 2005.
  11. S. R. Arashloo, "Multiscale Binarised Statistical Image Features for Symmetric Face Matching Using Multiple Descriptor Fusion Based on Class-Specific LDA," Pattern Analysis and Applications, May, 2015. (Published online).
  12. C. Carlet, G. Gao, and W. Liu, "A Secondary Construction and a Transformation on Rotation Symmetric Functions, and Their Action on Bent and Semi-Bent Functions," Combinatorial Theory, Series A, Vol.127, pp.161-175, Sept., 2014. https://doi.org/10.1016/j.jcta.2014.05.008
  13. M. Sonka, V. Hlavac, and R. Boyle, "Image Processing, Analysis, and Machine Vision," 4th ed., Cengage Learning, 2014.
  14. Y.-S. Moon and W.-K. Loh, "Triangular Inequality-based Rotation-Invariant Boundary Image Matching for Smart Devices," Multimedia Systems, Vol.21, Issue.1, pp.15-28, Feb., 2015. https://doi.org/10.1007/s00530-014-0380-2
  15. G. C. Oscos, T. M. Khoshgoftaar, and R. Wald, "Rotation Invariant Face Recognition Survey," in Proc. of the 15th Int'l Conf. on Information Reuse and Integration, Redwood City, California, pp.835-840, Aug., 2014.
  16. G. Lian, "Rotation Invariant Color Texture Classification Using Multiple Sub-DLBPs," Visual Communication and Image Representation, Vol.31, pp.1-13, Aug., 2015. https://doi.org/10.1016/j.jvcir.2015.05.003
  17. 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," in Proc. of Int'l Conf. on Management of Data, ACM SIGMOD, Athens, Greece, pp.457-468, June, 2011.
  18. M. Pawlik and N. Augsten, "A Memory-Efficient Tree Edit Distance Algorithm," in Proc. of the 25th Int'l Conf. on Database and Expert Systems Applications, Munich, Germany, Part I, pp.196-210, Sept., 2014.
  19. W.-K. Loh, S.-P. Kim, S.-K. Hong, and Y.-S. Moon, "Envelope-based Boundary Image Matching for Smart Devices Under Arbitrary Rotations," Multimedia Systems, Vol.21, Issue.1, pp.29-47, Feb., 2015. https://doi.org/10.1007/s00530-014-0386-9