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A Fast and Effective Image Geometric Verification Method for Efficient CBIR

  • Ling-Bo KongEmail author
  • Ling-Hai Kong
  • Tao Yang
  • Wei Lu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9093)

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.

Keywords

CBIR Geometric verification SIFT RANSAC Delaunay triangulation 

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References

  1. 1.
  2. 2.
  3. 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. 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) CrossRefGoogle Scholar
  5. 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. 6.
    Choi, S., Kim, T., Yu, W.: Performance evaluation of RANSAC family. In: Proc. BMVC, pp. 110–119 (2009)Google Scholar
  7. 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) CrossRefGoogle Scholar
  8. 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. 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)CrossRefMathSciNetGoogle Scholar
  10. 10.
    Jégou, H., Zisserman, A.: Triangulation embedding and democratic aggregation for image search. In: Proc. CVPR, pp. 3310–3317 (2014)Google Scholar
  11. 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)CrossRefzbMATHGoogle Scholar
  12. 12.
    Lowe, D.: Distinctive image features from scale-invariant keypoints. IJCV 60(2), 91–110 (2004)CrossRefGoogle Scholar
  13. 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. 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. 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) CrossRefGoogle Scholar
  16. 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)CrossRefGoogle Scholar
  17. 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. 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. 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) CrossRefGoogle Scholar
  20. 20.
    Vedaldi, A., Fulkerson, B.: VLFeat: An open and portable library of computer vision algorithms (2008)Google Scholar
  21. 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. 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)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.School of Software EngineeringBeiJing JiaoTong UniversityBeiJingChina
  2. 2.IAPCMBeiJingChina
  3. 3.TshingHua UniversityBeiJingChina

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