Skip to main content

Advertisement

Springer Nature Link
Log in

A quick scale-invariant interest point detecting approach

  • Original Paper
  • Published:
Machine Vision and Applications Aims and scope Submit manuscript

Abstract

To improve the real-time performance, a quick scale-invariant interest point detecting approach based on the image color information is proposed in this paper. The approach uses the scale normalized Laplacian operator to extract the interest points in the incomplete image pyramid. A new local descriptor is presented in the approach to compute the feature vector of each interest point. The descriptor is made up with several subregions like the SIFT (Scale-Invariant Feature Transform) descriptor, meanwhile, it chooses the mean values of different color components in each subregion as the feature vector’s elements to differentiate color objects better and reduce the descriptor’s dimension. Through the experiment, the detected interest points are robust to many image transformations and the approach is indicative of needing less computation than other interest point detecting algorithms. The research discloses that the approach can obtain both superior stability and real-time performance at the same time.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Mikolajczyk, K., Schmid, C.: Indexing based on scale invariant interest points. In: Proceedings of the 8th IEEE International Conference on Computer Vision, vol. 1, pp. 525–531. Vancouver, BC, Canada (2001)

  2. Brown, M., Lowe, D. G.: Recognising panoramas. In: Proceedings of the 9th IEEE International Conference on Computer Vision, vol. 2, pp. 1218–1225. Nice, France (2003)

  3. Se S., Lowe D.G., Little J.J.: Vision-based global localization and mapping for mobile robots. IEEE Trans. Robot. 21(3), 364–375 (2005)

    Article  Google Scholar 

  4. Wang J.Q., Zha H.B., Cipolla R.: Coarse-to-fine vision-based localization by indexing scale-invariant features. IEEE Trans. Syst. Man Cybernet. Part B Cybernet. 36(2), 413–422 (2006)

    Article  Google Scholar 

  5. Harris, C., Stephens, M.: A combined corner and edge detector. In: Proceedings of the 4th alvey vision conference, pp. 147–151. Manchester, UK (1988)

  6. Schmid C., Mohr R.: Local grayvalue invariants for image retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 19(5), 530–535 (1997)

    Article  Google Scholar 

  7. Johansson, B., Moe, A.: Patch-duplets for object recognition and pose estimation. In: Proceedings of the 2nd Canadian Conference on Computer and robot Vision, pp. 9–16. Victoria, Canada (2005)

  8. Montesinos, P., Gouet, V., Deriche, R.: Differential invariants for color image. In: Proceedings of the 14th International Conference on Pattern Recognition, vol. 1, pp. 838–840. Brisbane, Australia (1998)

  9. Dufournaud, Y., Schmid, C., Horaud, R.: Matching images with different resolutions. In: Proceedings of the 2000 IEEE Conference on Computer Vision and Pattern Recognition, pp. 612–618. Hilton Head Island, USA (2000)

  10. Lowe D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)

    Article  Google Scholar 

  11. Lindeberg T.: Feature detection with automatic scale selection. Int. J. Comput. Vis. 30(2), 79–116 (1998)

    Article  Google Scholar 

  12. Alvarez L., Morales F.: Affine morphological multiscale analysis of corners and multiple junctions. Int. J. Comput. Vis. 25(2), 95–107 (1997)

    Article  Google Scholar 

  13. Baumberg, A.: Reliable feature matching across widely separated views. In: Proceedings of the 2000 IEEE Conference on Computer Vision and Pattern Recognition, pp. 774–781. Hilton Head Island, USA (2000)

  14. Mikolajczyk, K., Schmid, C.: An affine invariant interest point detector. In: Proceedings of the 7th European Conference on Computer Vision, pp. 128–142. Copenhagen, Denmark (2002)

  15. Freeman W.T., Adelson E.H.: The design and use of steerable filters. IEEE Trans. Pattern Anal. Mach. Intell. 13(9), 891–906 (1991)

    Article  Google Scholar 

  16. Johnson, A. E., Hebert, M.: Recognizing objects by matching oriented points. In: Proceedings of the 1997 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 684–689. San Juan, Puerto Rico (1997)

  17. Carneiro, G., Jepson, A. D.: Phase-based local features. In: Proceedings of the 7th European Conference on Computer Vision, pp. 104–118. Copenhagen, Denmark (2002)

  18. Carneiro, G., Jepson, A. D.: Multi-scale phase-based local features. In: Proceedings of the 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 736–743. Madison, USA (2003)

  19. Belongie S., Malik J., Puzicha J.: Shape matching and object recognition using shape contexts. IEEE Trans. Pattern Anal. Mach. Intell. 24(4), 509–522 (2002)

    Article  Google Scholar 

  20. Yan, K., Sukthankar, R.: PCA-SIFT: a more distinctive representation for local image descriptors. In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 506–513. Washington, USA (2004)

  21. Mikolajczyk K., Schmid C.: A performance evaluation of local descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 27(10), 1615–1630 (2005)

    Article  Google Scholar 

  22. Swain M.J., Ballard D.H.: Color indexing. Int. J. Comput. Vis. 7(1), 11–32 (1991)

    Article  Google Scholar 

  23. Funt B.V., Finlayson G.D.: Color constant color indexing. IEEE Trans. Pattern Anal. Mach. Intell. 17(5), 522–529 (1995)

    Article  Google Scholar 

  24. Adjeroh D.A., Lee M.C.: On ratio-based color indexing. IEEE Trans. Image Process. 10(1), 36–48 (2001)

    Article  MATH  Google Scholar 

  25. Slater D., Healey G.: The illumination-invariant recognition of 3D objects using local color invariants. IEEE Trans. Pattern Anal. Mach. Intell. 18(2), 206–210 (1996)

    Article  Google Scholar 

  26. Schmid, C., Mohr, R., Bauckhage, C.: Comparing and evaluating interest points. In: Proceedings of the 6th International Conference on Computer Vision, pp. 230–235. Bombay, India (1998)

  27. Schmid C., Mohr R., Bauckhage C.: Evaluation of interest point detectors. Int. J. Comput. Vis. 37(2), 151–172 (2000)

    Article  MATH  Google Scholar 

  28. Gao, J., Huang, X. H., Peng, G., et al: Color-based scale-invariant feature detection applied in robot vision. In: Proceedings of the 5th International Symposium on Multispectral Image processing and Pattern Recognition, vol. 6790, pp. 4E1–4E8. Wuhan, China (2007)

  29. Lindeberg T.: Scale-space theory: a basic tool for analysing structures at different scales. J. Appl. Stat. 21(2), 225–270 (1994)

    Article  Google Scholar 

  30. Crowley, J.L.: A Representation for Visual Information. pp. 44–46. Carnegie-Mellon University, Pittsburgh (1981)

  31. Friedman J.H., Bentley J.L., Finkel R.A.: An algorithm for finding best matches in logarithmic expected time. ACM Trans. Math. Softw. 3(3), 209–226 (1977)

    Article  MATH  Google Scholar 

  32. Beis, J. S., Lowe, D. G.: Shape indexing using approximate nearest-neighbour search in high-dimensional spaces. In: Proceedings of the 1997 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1000–1006. San Juan, Puerto Rico (1997)

  33. Fishler M.A., Bolles R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24(6), 381–395 (1981)

    Article  Google Scholar 

  34. Forsyth, D.A., Ponce, J.: Computer Vision: A Modern Approach. pp. 346–351. Pearson Education Asia Limited and Tsinghua University Press, Beijing (2004)

Download references

Author information

Authors and Affiliations

  1. Key Laboratory of Ministry of Education for Image Processing and Intelligent Control, Department of CSE, HUST, 430074, Wuhan, China

    Jian Gao & Xinhan Huang

  2. BFSU, 100089, Beijing, China

    Bo Liu

Authors
  1. Jian Gao
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Xinhan Huang
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Bo Liu
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Jian Gao.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Gao, J., Huang, X. & Liu, B. A quick scale-invariant interest point detecting approach. Machine Vision and Applications 21, 351–364 (2010). https://doi.org/10.1007/s00138-008-0167-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00138-008-0167-6

Keywords