Skip to main content
SpringerLink
Log in

Combining Multiple Nearest-Neighbor Searches for Multiscale Feature Point Matching

  • Conference paper
  • First Online:
Book cover Image Processing and Communications Challenges 7

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 389))

  • 710 Accesses

Abstract

Multiscale feature point descriptors are used to describe the surrounding of image feature points, taking into account neighboring image details at various levels. The feature vectors are divided into several parts, each of which describes surrounding at increasing distance from the feature point. In this paper a method for matching such descriptors is proposed. It is based on multiple nearest-neighbors searches that are applied to match parts of the descriptor, followed by combining the partial matching results into final indication of the closest descriptor.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Alahi, A., Ortiz, R., Vandergheynst, P.: FREAK: fast retina keypoint. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 510–517 (2012)

    Google Scholar 

  2. Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (SURF). Comput. Vis. Image Underst. 110(3), 346–359 (2008) (similarity Matching in Computer Vision and Multimedia)

    Google Scholar 

  3. Broder, A.Z., Charikar, M., Frieze, A.M., Mitzenmacher, M.: Min-wise independent permutations. J. Comput. Syst. Sci. 60, 327–336 (1998)

    MathSciNet  MATH  Google Scholar 

  4. Calonder, M., Lepetit, V., Strecha, C., Fua, P.: Brief: Binary robust independent elementary features. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) Computer Vision—ECCV 2010. Lecture Notes in Computer Science, vol. 6314, pp. 778–792. Springer, Berlin (2010)

    Google Scholar 

  5. Chum, O., Matas, J.: Fast computation of min-hash signatures for image collections. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3077–3084 (2012)

    Google Scholar 

  6. Harris, C., Stephens, M.: A combined corner and edge detector. In: Proceedings of Fourth Alvey Vision Conference, pp. 147–151 (1988)

    Google Scholar 

  7. Leutenegger, S., Chli, M., Siegwart, R.: BRISK: binary robust invariant scalable keypoints. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 2548–2555 (2011)

    Google Scholar 

  8. Lowe, D.: Object recognition from local scale-invariant features. In: The Proceedings of the Seventh IEEE International Conference on Computer Vision, vol. 2, pp. 1150–1157 (1999)

    Google Scholar 

  9. Muja, M., Lowe, D.G.: Fast approximate nearest neighbors with automatic algorithm configuration. In: International Conference on Computer Vision Theory and Application (VISSAPP’09), pp. 331–340. INSTICC Press (2009)

    Google Scholar 

  10. Muja, M., Lowe, D.G.: Fast matching of binary features. In: Proceedings of the 2012 Ninth Conference on Computer and Robot Vision. pp. 404–410. CRV ’12, IEEE Computer Society, Washington, DC, USA (2012)

    Google Scholar 

  11. Rosten, E., Porter, R., Drummond, T.: FASTER and better: a machine learning approach to corner detection. IEEE Trans. Pattern Anal. Mach. Intell. 32, 105–119 (2010)

    Google Scholar 

  12. Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: ORB: an efficient alternative to SIFT or SURF. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 2564–2571 (2011)

    Google Scholar 

  13. Salakhutdinov, R., Hinton, G.: Semantic hashing. Int. J. Approximate Reasonig 50(7), 969–978 (2009)

    Article  Google Scholar 

  14. Slaney, M., Casey, M.: Locality-sensitive hashing for finding nearest neighbors. Sig. Process. Mag. IEEE 25(2), 128–131 (2008)

    Article  Google Scholar 

  15. Smith, S., Brady, J.: SUSAN, a new approach to low level image processing. Int. J. Comput. Vis. 23(1), 45–78 (1997)

    Article  Google Scholar 

  16. Tang, F., Tao, H.: Fast multi-scale template matching using binary features. In: IEEE Workshop on Applications of Computer Vision, WACV ’07, pp. 36–36 (2007)

    Google Scholar 

Download references

Acknowledgments

This work was co-financed by the European Union within the European Regional Development Fund.

Author information

Authors and Affiliations

  1. Institute of Control and Industrial Electronics, University of Technology, Ul.Koszykowa 75, 00-662, Warszawa, Poland

    Michał Swiercz, Marcin Iwanowski & Arkadiusz Cacko

  2. Lingaro, Ul. Puławska 99a, 02-595, Warszawa, Poland

    Michał Swiercz & Grzegorz Sarwas

Authors
  1. Michał Swiercz
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marcin Iwanowski
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Grzegorz Sarwas
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Arkadiusz Cacko
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Michał Swiercz .

Editor information

Editors and Affiliations

  1. Bydgoszcz, Poland

    Ryszard S. Choraś

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Swiercz, M., Iwanowski, M., Sarwas, G., Cacko, A. (2016). Combining Multiple Nearest-Neighbor Searches for Multiscale Feature Point Matching. In: Choraś, R. (eds) Image Processing and Communications Challenges 7. Advances in Intelligent Systems and Computing, vol 389. Springer, Cham. https://doi.org/10.1007/978-3-319-23814-2_27

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-23814-2_27

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23813-5

  • Online ISBN: 978-3-319-23814-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation