Skip to main content

A Fast and Effective Image Geometric Verification Method for Efficient CBIR

  • Conference paper
  • First Online:
Databases Theory and Applications (ADC 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9093))

Included in the following conference series:

Abstract

Along with the widespread use of IT techniques, the requirements for CBIR (Content-Based Image Retrieval) is attractive for researchers from diverse areas. CBIR’s challenge is still how to ensure the meaningfulness of the retrieved images, for which the geometric consistency should be considered. And RANSAC and its variants are popular in the post-verification stage for that. This paper presents a Delaunay triangulation (DT) based method for that, some properties of which ensure its stability to capture the local structures. By converting the geometric verification into DT mapping, our method could not only catch invariant local structure points, but also is much more efficient (\(O(Nlog(N))\)). We evaluate our approach on common image benchmark and demonstrate its effectiveness for image geometric verification problem.

L.-B. Kong — This work was supported by the China Fundamental Research Funds for the Central Universities under Grant No.2011JBM320, and the National Natural Science Foundation of China (NSFC) under Grant No.61272353.

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

Access this chapter

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

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. http://www.robots.ox.ac.uk/~vgg/data/oxbuildings/index.html

  2. http://lebeda.sk/DP/index.xhtml

  3. Attali, D., Boissonnat, J.-D., Lieutier, A.: Complexity of the delaunay triangulation of points on surfaces: the smooth case. In: 19th Annual Symposium on Computational Geometry, pp. 201–210 (2003)

    Google Scholar 

  4. Bhattacharya, P., Gavrilova, M.: DT-RANSAC: A Delaunay Triangulation Based Scheme for Improved RANSAC Feature Matching. In: Gavrilova, M.L., Tan, C.J.K., Kalantari, B. (eds.) Transactions on Computational Science XX. LNCS, vol. 8110, pp. 5–21. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  5. Cao, Y., Wang, C., Li, Z., Zhang, L., Zhang, L.: Spatial bag-of-features. In: Proc. CVPR, pp. 3352–3359 (2012)

    Google Scholar 

  6. Choi, S., Kim, T., Yu, W.: Performance evaluation of RANSAC family. In: Proc. BMVC, pp. 110–119 (2009)

    Google Scholar 

  7. Chum, O., Matas, J., Kittler, J.: Locally optimized RANSAC. In: Michaelis, B., Krell, G. (eds.) DAGM 2003. LNCS, vol. 2781, pp. 236–243. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  8. Chum, O., Mikulík, A., Perdoch, M., Matas, J.: Total rrecall II: Query expansion revisited. In: Proc. CVPR, pp. 889–896 (2011)

    Google Scholar 

  9. Fischler, M.A., Bolles, R.C.: Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Comm. of the ACM 24, 381–395 (1981)

    Article  MathSciNet  Google Scholar 

  10. Jégou, H., Zisserman, A.: Triangulation embedding and democratic aggregation for image search. In: Proc. CVPR, pp. 3310–3317 (2014)

    Google Scholar 

  11. Liu, Y., Zhang, D.S., Lu, G.J., Ma, W.Y.: A survey of content-based image retrieval with high-level semantics. Pattern Recognition 40, 262–282 (2007)

    Article  MATH  Google Scholar 

  12. Lowe, D.: Distinctive image features from scale-invariant keypoints. IJCV 60(2), 91–110 (2004)

    Article  Google Scholar 

  13. Philbin, J., Chum, O., Isard, M., Sivic, J., Zisserman, A.: Object retrieval with large vocabularies and fast spatial matching. In: Proc. CVPR, pp. 1–8 (2007)

    Google Scholar 

  14. Qin, D.F., Gammeter, S., Bossard, L., Quack, T., Van Gool, L.: Hello neighbor: accurate object retrieval with k-reciprocal nearest neighbors. In: Proc. CVPR, pp. 777–784 (2011)

    Google Scholar 

  15. Raguram, R., Frahm, J.-M., Pollefeys, M.: A comparative analysis of RANSAC techniques leading to adaptive real-time random sample consensus. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part II. LNCS, vol. 5303, pp. 500–513. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  16. Rui, Y., Huang, T.S., Chang, S.F.: Image retrieval: current techniques, promising directions, and open issues. J. Visual Commun. Image Representation 10(4), 39–62 (1999)

    Article  Google Scholar 

  17. Shen, X.H., Lin, Z., Brandt, J., Avidan, S., Wu, Y.: Object retrieval and localization with spatially-constrained similarity measure and k-nn re-ranking. In: Proc. CVPR, pp. 3013–3020 (2012)

    Google Scholar 

  18. Sivic, J., Zisserman, A.: Video google: a text retrieval approach to object matching in videos. In: Proc. ICCV, pp. 1470–1477 (2003)

    Google Scholar 

  19. Tolias, G., Furon, T., Jégou, H.: Orientation covariant aggregation of local descriptors with embeddings. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part VI. LNCS, vol. 8694, pp. 382–397. Springer, Heidelberg (2014)

    Chapter  Google Scholar 

  20. Vedaldi, A., Fulkerson, B.: VLFeat: An open and portable library of computer vision algorithms (2008)

    Google Scholar 

  21. Zhang, Y.M., Jia, Z.Y., Chen, T.: Image retrieval with geometry-preserving visual phrases. In: Proc. CVPR, pp. 809–816 (2011)

    Google Scholar 

  22. Zhao, X.Y., He, Z.X., Zhang, S.Y.: Improved keypoint descriptors based on delaunay triangulation for image matching. Optik 125, 3121–3123 (2014)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ling-Bo Kong .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Kong, LB., Kong, LH., Yang, T., Lu, W. (2015). A Fast and Effective Image Geometric Verification Method for Efficient CBIR. In: Sharaf, M., Cheema, M., Qi, J. (eds) Databases Theory and Applications. ADC 2015. Lecture Notes in Computer Science(), vol 9093. Springer, Cham. https://doi.org/10.1007/978-3-319-19548-3_19

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-19548-3_19

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19547-6

  • Online ISBN: 978-3-319-19548-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics