Skip to main content

Measuring the Coverage of Interest Point Detectors

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 6753))

Abstract

Repeatability is widely used as an indicator of the performance of an image feature detector but, although useful, it does not convey all the information that is required to describe performance. This paper explores the spatial distribution of interest points as an alternative indicator of performance, presenting a metric that is shown to concur with visual assessments. This metric is then extended to provide a measure of complementarity for pairs of detectors. Several state-of-the-art detectors are assessed, both individually and in combination. It is found that Scale Invariant Feature Operator (SFOP) is dominant, both when used alone and in combination with other detectors.

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

Buying options

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 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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Lowe, D.: Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision 60, 91–110 (2004)

    Article  Google Scholar 

  2. Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-Up Robust Features (SURF). Computer Vision and Image Understanding 110, 346–359 (2008)

    Article  Google Scholar 

  3. Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust Wide Baseline Stereo from Maximally Stable Extremal Regions. In: BMVC, Cardiff, UK, pp. 384–393 (2002)

    Google Scholar 

  4. Kadir, T., Zisserman, A., Brady, M.: An affine invariant salient region detector. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3021, pp. 228–241. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  5. Tuytelaars, T., Mikolajczyk, K.: Local Invariant Feature Detectors: A Survey. Foundations and Trends in Computer Graphics and Vision 3, 177–280 (2007)

    Article  Google Scholar 

  6. Forstner, W., Dickscheid, T., Schindler, F.: Detecting Interpretable and Accurate Scale-Invariant Keypoints. In: ICCV, Kyoto, Japan, pp. 2256–2263 (2009)

    Google Scholar 

  7. Schmid, C., Mohr, R., Bauckhage, C.: Evaluation of Interest Point Detectors. International Journal of Computer Vision 37, 151–172 (2000)

    Article  MATH  Google Scholar 

  8. Ehsan, S., Kanwal, N., Clark, A., McDonald-Maier, K.: Improved Repeatability Measures for Evaluating Performance of Feature Detectors. Electronics Letters 46, 998–1000 (2010)

    Article  Google Scholar 

  9. Nowak, E., Jurie, F., Triggs, B.: Sampling Strategies for Bag-of-Features Image Classification. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3954, pp. 490–503. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  10. Perdoch, M., Matas, J., Obdrzalek, S.: Stable Affine Frames on Isophotes. In: ICCV, Rio deJaneiro, Brazil (2007)

    Google Scholar 

  11. Tuytelaars, T.: Dense Interest Points. In: CVPR, San Francisco, USA, pp. 2281–2288 (2010)

    Google Scholar 

  12. Dickscheid, T., Förstner, W.: Evaluating the Suitability of Feature Detectors for Automatic Image Orientation Systems. In: Fritz, M., Schiele, B., Piater, J.H. (eds.) ICVS 2009. LNCS, vol. 5815, pp. 305–314. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  13. Dickscheid, T., Schindler, F., Förstner, W.: Coding Images with Local Features. International Journal of Computer Vision (2010), doi: 10.1007/s11263-010-0340-z

    Google Scholar 

  14. Lazebnik, S., Schmid, C., Ponce, J.: Sparse Texture Representation using Affine-Invariant Neighborhoods. In: IEEE CVPR, Wisconsin, USA, pp. 319–324 (June 2003)

    Google Scholar 

  15. Mikolajczyk, K., Leibe, B., Schiele, B.: Multiple Object Class Detection with a Generative Model. In: IEEE CVPR, New York, USA, pp. 26–36 (2006)

    Google Scholar 

  16. Sivic, J., Zisserman, A.: Video Google: A Text Retrieval Approach to Object Matching in Videos. In: ICCV, Nice, France, vol. 2, pp. 1470–1477 (2003)

    Google Scholar 

  17. Zhang, B., Hsu, M., Dayal, U.: K-Harmonic Means-A Spatial Clustering Algorithm with Boosting. In: Roddick, J., Hornsby, K.S. (eds.) TSDM 2000. LNCS (LNAI), vol. 2007, pp. 31–45. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  18. Oxford Data Sets, http://www.robots.ox.ac.uk/~vgg/research/affine/

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Ehsan, S., Kanwal, N., Clark, A.F., McDonald-Maier, K.D. (2011). Measuring the Coverage of Interest Point Detectors. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2011. Lecture Notes in Computer Science, vol 6753. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21593-3_26

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-21593-3_26

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-21592-6

  • Online ISBN: 978-3-642-21593-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics