Journal of the Korea Society of Computer and Information (한국컴퓨터정보학회논문지)

Korean Society of Computer Information (한국컴퓨터정보학회)
권호 표지
DOI QR코드

DOI QR Code

Design and Implementation of a Boundary Matching System Supporting Partial Denoising for Large Image Databases

  • Kim, Bum-Soo (Dept. of Future Technology and Convergence Research, Korea Institute of Civil Engineering and Building Technology) ;
  • Kim, Jin-Uk (Dept. of Future Technology and Convergence Research, Korea Institute of Civil Engineering and Building Technology)
  • Received : 2019.04.02
  • Accepted : 2019.04.25
  • Published : 2019.05.31
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper, we design and implement a partial denoising boundary matching system using indexing techniques. Converting boundary images to time-series makes it feasible to perform a fast search using indexes even on a very large image database. Thus, using this converting method we develop a client-server system based on the previous partial denoising research in the GUI(graphical user interface) environment. The client first converts a query image given by a user to a time-series and sends denoising parameters and the tolerance with this time-series to the server. The server identifies similar images from the index by evaluating a range query, which is constructed using inputs given from the client and sends the resulting images to the client. Experimental results show that our system provides many intuitive and accurate matching results.

Keywords

CPTSCQ_2019_v24n5_35_f0001.png 이미지

Fig. 1. Examples of various studies on boundary matching.

CPTSCQ_2019_v24n5_35_f0002.png 이미지

Fig. 2. An Example of converting an image to a time-series by CCD [4].

CPTSCQ_2019_v24n5_35_f0003.png 이미지

Fig. 3. An overall architecture of an index-based system for partial denoising boundary matching.

CPTSCQ_2019_v24n5_35_f0004.png 이미지

Fig. 4. Example screenshots of the proposed boundary matching system.

CPTSCQ_2019_v24n5_35_f0005.png 이미지

Fig. 5. The scalability of the naive matching and the index-based matching algorithms.

References

  1. 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
  2. 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
  3. 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, No. 1, pp. 29-47, Feb. 2015. https://doi.org/10.1007/s00530-014-0386-9
  4. B.-S. Kim, Y.-S. Moon, and J.-G. Lee, "Boundary Image Matching Supporting Partial Denoising Using Time- Series Matching Techniques," Multimedia Tools and Applications, Vol. 76, No. 6, pp. 8471-8496, Mar. 2017. https://doi.org/10.1007/s11042-016-3479-y
  5. N. Beckmann, H.-P. Kriegel, R. Schneider, and B. Seeger, "The R∗-tree: An Efficient and Robust Access Method for Points and Rectangles," In Proc. of the ACM SIGMOD Int'l Conf. on Management of Data, pp. 322-331, Atlantic City, New Jersey, May 1990.
  6. R. Agrawal, C. Faloutsos, and A. Swami, "Efficient Similarity Search in Sequence Databases," In Proc. the 4th Int'l Conf. on Foundations of Data Organization and Algorithms, pp. 69-84, Chicago, Illinois, Oct. 1993.
  7. C. Faloutsos, M. Ranganathan, and Y. Manolopoulos, "Fast Subsequence Matching in Time-Series Databases," In Proc. of the ACM SIGMOD Int'l Conf. on Management of Data, pp. 419-429, Minneapolis, Minnesota, May 1994.
  8. S. Belongie, J. Malik, J. Puzicha "Shape Matching and Object Recognition Using Shape Contexts," IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 24, No. 4, pp. 509-522, Apr. 2002. https://doi.org/10.1109/34.993558
  9. R. Datta, D. Joshi, J. Li, and J. Z. Wang, "Image Retrieval: Ideas, Influences, and Trends of the New Age," ACM Computing Surveys, Vol. 40, No. 2, pp. 34-94, Apr. 2008.
  10. M. Sonka, V. Hlavac, and R. Boyle, Image Processing, Analysis, and Machine Vision, 4th Ed., Cengage Learning, 2014.
  11. D. Zhang and G. Lu, "Review of Shape Representation and Description Techniques," Pattern Recognition, Vol. 37, No. 1, pp. 1-19, Jan. 2004. https://doi.org/10.1016/j.patcog.2003.07.008
  12. S. Berchtold, C. Bohm, and H.-P. Kriegel, "The Pyramid-Technique: Towards Breaking the Curse of Dimensionality," In Proc. of the ACM SIGMOD Int'l Conf. on Management of Data, pp. 142-153, Seattle, Washington, June 1998.
  13. R. Arandjelovic and A. Zisserman, "Three Things Everyone Should Know to Improve Object Retrieval," In Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, pp. 2911-2918, Providence, Rhode Island, June 2012.