Skip to main content
SpringerLink
Log in

Robust Binary Feature Using the Intensity Order

  • Conference paper
  • First Online:
Computer Vision – ACCV 2014 (ACCV 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9003))

Included in the following conference series:

Abstract

Binary features have received much attention with regard to memory and computational efficiency with the emerging demands in the mobile and embedded vision systems fields. In this context, we present a robust binary feature using the intensity order. By analyzing feature regions, we devise a simple but effective strategy to detect keypoints. We adopt an ordinal description and encode the intensity order into a binary descriptor with proper binarization. As a result, our method obtains high repeatability and shows better performance with regard to feature matching with much less storage usage than other conventional features. We evaluate the performance of the proposed binary feature with various experiments, demonstrate its efficiency in terms of storage and computation time, and show its robustness under various geometric and photometric transformations.

Y. Choi and C. Park—The first and the second authors provided equal contributions to this work.

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 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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

Notes

  1. 1.

    http://computer-vision-talks.com/2011/08/feature-descriptor-comparison-report/.

References

  1. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. (IJCV) 60, 91–110 (2004)

    Article  Google Scholar 

  2. Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (surf). Comput. Vis. Image Underst. (CVIU) 110, 346–359 (2008)

    Article  Google Scholar 

  3. Chandrasekhar, V., Takacs, G., Chen, D.M., Tsai, S.S., Reznik, Y.A., Grzeszczuk, R., Girod, B.: Compressed histogram of gradients: A low-bitrate descriptor. Int. J. Comput. Vis. (IJCV) 96, 384–399 (2012)

    Article  Google Scholar 

  4. Sattler, T., Leibe, B., Kobbelt, L.: Fast image-based localization using direct 2d-to-3d matching. In: Proceedings of International Conference on Computer Vision (ICCV), pp. 667–674 (2011)

    Google Scholar 

  5. Li, Y., Snavely, N., Huttenlocher, D., Fua, P.: Worldwide pose estimation using 3D point clouds. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part I. LNCS, vol. 7572, pp. 15–29. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  6. Calonder, M., Lepetit, V., Strecha, C., Fua, P.: BRIEF: binary robust independent elementary features. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 778–792. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  7. Rublee, E., Rabaud, V., Konolige, K., Bradski, G.R.: Orb: An efficient alternative to sift or surf. In: Proceedings of International Conference on Computer Vision (ICCV), pp. 2564–2571 (2011)

    Google Scholar 

  8. Leutenegger, S., Chli, M., Siegwart, R.: Brisk: Binary robust invariant scalable keypoints. In: Proceedings of International Conference on Computer Vision (ICCV), pp. 2548–2555 (2011)

    Google Scholar 

  9. Heinly, J., Dunn, E., Frahm, J.-M.: Comparative evaluation of binary features. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part II. LNCS, vol. 7573, pp. 759–773. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

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

    Article  Google Scholar 

  11. Toews, M., Wells III, W.: Sift-rank: Ordinal description for invariant feature correspondence. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 172–177 (2009)

    Google Scholar 

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

    Google Scholar 

  13. Mikolajczyk, K., Schmid, C.: Scale and affine invariant interest point detectors. Int. J. Comput. Vis. (IJCV) 60, 63–86 (2004)

    Article  Google Scholar 

  14. Rosten, E., Drummond, T.: Machine learning for high-speed corner detection. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006, Part I. LNCS, vol. 3951, pp. 430–443. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

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

    Article  Google Scholar 

  16. Mair, E., Hager, G.D., Burschka, D., Suppa, M., Hirzinger, G.: Adaptive and generic corner detection based on the accelerated segment test. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part II. LNCS, vol. 6312, pp. 183–196. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  17. Agrawal, M., Konolige, K., Blas, M.R.: CenSurE: center surround extremas for realtime feature detection and matching. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part IV. LNCS, vol. 5305, pp. 102–115. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  18. Wang, Z., Fan, B., Wu, F.: Frif:fast robust invariant feature. In: Proceedings of British Machine Vision Conference (BMVC) (2013)

    Google Scholar 

  19. Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Trans. Pattern Anal. Mach. Intell. (PAMI) 27, 1615–1630 (2005)

    Article  Google Scholar 

  20. Tola, E., Lepetit, V., Fua, P.: Daisy: An efficient dense descriptor applied to wide-baseline stereo. IEEE Trans. Pattern Anal. Mach. Intell. (PAMI) 32, 815–830 (2010)

    Article  Google Scholar 

  21. Ziegler, A., Christiansen, E.M., Kriegman, D.J., Belongie, S.J.: Locally uniform comparison image descriptor. In: Neural Information Processing Systems (NIPS), pp. 1–9 (2012)

    Google Scholar 

  22. Wang, Z., Fan, B., Wu, F.: Local intensity order pattern for feature description. In: Proceedings of International Conference on Computer Vision (ICCV), pp. 603–610 (2011)

    Google Scholar 

  23. Alahi, A., Ortiz, R., Vandergheynst, P.: Freak: Fast retina keypoint. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 510–517 (2012)

    Google Scholar 

  24. Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., Gool, L.V.: A comparison of affine region detectors. Int. J. Comput. Vis. (IJCV) 65, 43–72 (2005)

    Article  Google Scholar 

Download references

Acknowledgement

We would like to thank Jungho Kim and Jiyoung Jung for their support. This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government (No. 2010-0028680).

Author information

Authors and Affiliations

  1. Robotics and Computer Vision Laboratory, KAIST, Daejeon, Korea

    Yukyung Choi, Chaehoon Park, Joon-Young Lee & In So Kweon

Authors
  1. Yukyung Choi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chaehoon Park
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Joon-Young Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. In So Kweon
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to In So Kweon .

Editor information

Editors and Affiliations

  1. Technische Universität München, Garching, Bayern, Germany

    Daniel Cremers

  2. University of Adelaide, Adelaide, South Australia, Australia

    Ian Reid

  3. Keio University, Yokohama, Kanagawa, Japan

    Hideo Saito

  4. University of California at Merced, Merced, California, USA

    Ming-Hsuan Yang

1 Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material (pdf 15,608 KB)

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Choi, Y., Park, C., Lee, JY., Kweon, I.S. (2015). Robust Binary Feature Using the Intensity Order. In: Cremers, D., Reid, I., Saito, H., Yang, MH. (eds) Computer Vision – ACCV 2014. ACCV 2014. Lecture Notes in Computer Science(), vol 9003. Springer, Cham. https://doi.org/10.1007/978-3-319-16865-4_37

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-16865-4_37

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16864-7

  • Online ISBN: 978-3-319-16865-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation