Skip to main content
SpringerLink
Log in

Rank Order Reduction Based Fast Pattern Matching Algorithm

  • Conference paper
  • First Online:
Computational Intelligence, Communications, and Business Analytics (CICBA 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 776))

  • 1377 Accesses

Abstract

This paper reports a fast pattern matching algorithm which makes use of K-NN (K-nearest neighbor) based rank order reduction approach to detect a pattern or object in a given image efficiently. Initially, the given image is divided into several candidate windows, each of size the input pattern. In the next step, both the input pattern and the candidate windows are characterized by Haar transform. From the characterization, Haar Projection Values (HPV) is determined. Further, rectangle sum on both input pattern and candidate windows is computed using integral image technique. Subsequently, by using sum of absolute difference (SAD) correlation distance between the input pattern and candidate windows is determined. In order to detect the pattern, rank order approach using K-NN is applied to determine the first k number of most similar candidate windows containing the input pattern. To reduce the computational complexity of selecting a perfectly matched window, again sum of absolute differences (SAD) is applied and this leads to select the best match pattern having the total object. Decoupling correlation measures also increase the accuracy of matching pattern. Finally, the input pattern is detected and localized in the given image. The pattern matching accuracy proves the efficacy of the proposed algorithm.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

References

  1. Brunelli, R.: Template Matching Techniques in Computer Vision: Theory and Practice, p. 348. Wiley, New York (2009)

    Book  Google Scholar 

  2. Alkhansari, M.G.: A fast globally optimal algorithm for template matching using low-resolution pruning. IEEE Trans. Image Process. 10(4), 526–533 (2001)

    Article  MATH  Google Scholar 

  3. Ouyang, W., Zhang, R., Cham, W.K.: Fast pattern matching using orthogonal Haar transform. In: IEEE Conference on Computer Vision and Pattern Recognition (2010)

    Google Scholar 

  4. http://www.cs.columbia.edu/CAVE/databases/

  5. Iain, E.G.R.: H.264 and MPEG-4 Video Compression: Video Coding for Next-generation Multimedia. Wiley, Chichester (2003)

    Google Scholar 

  6. Hart, P.E.: The condensed nearest neighbor rule. IEEE Trans. Inf. Theory 18, 515–516 (1968)

    Article  Google Scholar 

  7. Shapiro, L.G., Stockman, G.C.: Computer Vision. Prentice Hall, Englewood Cliffs (2001)

    Google Scholar 

  8. Li, H., Kim, E., Huang, X., He, L.: Object matching with a locally affine-invariant constraint. In: International Conference on Computer Vision and Pattern Recognition, pp. 1641–1648 (2010)

    Google Scholar 

  9. Fitzgibbon, A., Wexler, Y., Zisserman, A.: Image-based rendering using image-based priors. Int. J. Comput. Vision 63(2), 141–151 (2005)

    Article  Google Scholar 

  10. Luczak, T., Szpankowski, W.: A suboptimal lossy data compression based on approximate pattern matching. IEEE Trans. Inform. Theory 43(5), 1439–1451 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  11. Dufour, R.M., Miller, E.L., Galatsanos, N.P.: Template matching based object recognition with unknown geometric parameters. IEEE Trans. Image Process. 11(12), 1385–1396 (2002)

    Article  MathSciNet  Google Scholar 

  12. Freeman, W.T., Jones, T.R., Pasztor, E.C.: Example-based super-resolution. IEEE Comput. Graph. Appl. 22(2), 56–65 (2002)

    Article  Google Scholar 

  13. Efros, A.A., Leung, T.K.: Texture synthesis by non-parametric sampling, in Computer Vision. In: IEEE International Conference on The Proceedings of the Seventh, vol. 2, pp. 1033–1038. IEEE (1999)

    Google Scholar 

  14. Mak, C.-M., Fong, C.-K., Cham, W.-K.: Fast motion estimation for H.264/AVC in Walsh-Hadamard domain. IEEE Trans. Circ. Syst. Video Technol. 18(6), 735–745 (2008)

    Article  Google Scholar 

  15. Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 2, pp. 60–65. IEEE (2005)

    Google Scholar 

  16. Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: Image denoising by sparse 3-D transform domain collaborative altering. IEEE Trans. Image Process. 16(8), 2080–2095 (2007)

    Article  MathSciNet  Google Scholar 

  17. Alon, Y., Ferencz, A., Shashua, A.: Off-road path following using region classification and geometric projection constraints. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 689–696. IEEE (2006)

    Google Scholar 

  18. Shin, Y., Ju, J.S., Kim, E.Y.: Welfare interface implementation using multiple facial features tracking for the disabled people. Pattern Recogn. Lett. 29(13), 1784–1796 (2008)

    Article  Google Scholar 

  19. Wang, Q., You, S.: Real-time image matching based on multiple view kernel projection. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2007, pp. 1–8. IEEE (2007)

    Google Scholar 

  20. Wu, X.: Template-based action recognition: classifying hockey players’ movement. PhD thesis, The University of British Columbia (2005)

    Google Scholar 

  21. Di Stefano, L., Mattoccia, S.: A sufficient condition based on the Cauchy-Schwarz inequality for efficient template matching. In: Proceedings, 2003 International Conference on Image Processing, ICIP 2003, vol. 1. IEEE (2003)

    Google Scholar 

  22. Di Stefano, L., Mattoccia, S.: Fast template matching using bounded partial correlation. Mach. Vis. Appl. 13(4), 213–221 (2003)

    Article  Google Scholar 

  23. Mattoccia, S., Tombari, F., Stefano, L.D.: Fast full-search equivalent template matching by enhanced bounded correlation. IEEE Trans. Image Process. 17(4), 528–538 (2008)

    Article  MathSciNet  Google Scholar 

  24. Goshtasby, A.A.: 2-D and 3-D Image Registration: for Medical, Remote Sensing, and Industrial Applications. Wiley, New York (2005)

    Google Scholar 

  25. Briechle, K., Hanebeck, U.D.: Template matching using fast normalized cross correlation. In: Aerospace, Defense Sensing, Simulation, and Controls, pp. 95–102. International Society for Optics and Photonics (2001)

    Google Scholar 

  26. Lewis, J.P.: Fast template matching. Vision Interface 95, 15–19 (1995)

    Google Scholar 

  27. Kadyrov, A., Petrou, M.: The invaders’ algorithm: range of values modulation for accelerated correlation. IEEE Trans. Pattern Anal. Mach. Intell. 28(11), 1882–1886 (2006)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology Durgapur, Durgapur, 713209, West Bengal, India

    Himanshu Jaiswal & Dakshina Ranjan Kisku

  2. Department of Information Technology, Neotia Institute of Technology, Management and Science, South 24 Paragana, Kolkata, West Bengal, India

    Deep Suman Dev

Authors
  1. Himanshu Jaiswal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Deep Suman Dev
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dakshina Ranjan Kisku
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dakshina Ranjan Kisku .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Kalyani, Kalyani, West Bengal, India

    J. K. Mandal

  2. Department of Computer and System Sciences, Visva Bharati University, Bolpur Santiniketan, West Bengal, India

    Paramartha Dutta

  3. Department of Information Technology, Calcutta Business School, Kolkata, India

    Somnath Mukhopadhyay

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer Nature Singapore Pte Ltd.

About this paper

Cite this paper

Jaiswal, H., Dev, D.S., Kisku, D.R. (2017). Rank Order Reduction Based Fast Pattern Matching Algorithm. In: Mandal, J., Dutta, P., Mukhopadhyay, S. (eds) Computational Intelligence, Communications, and Business Analytics. CICBA 2017. Communications in Computer and Information Science, vol 776. Springer, Singapore. https://doi.org/10.1007/978-981-10-6430-2_24

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-6430-2_24

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-6429-6

  • Online ISBN: 978-981-10-6430-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation