How to Use SIFT Vectors to Analyze an Image with Database Templates

  • Adrien Auclair
  • Laurent D. Cohen
  • Nicole Vincent
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4918)

Abstract

During last years, local image descriptors have received much attention because of their efficiency for several computer vision tasks such as image retrieval, image comparison, features matching for 3D reconstruction... Recent surveys have shown that Scale Invariant Features Transform (SIFT) vectors are the most efficient for several criteria. In this article, we use these descriptors to analyze how a large input image can be decomposed by small template images contained in a database. Affine transformations from database images onto the input image are found as described in [16]. The large image is thus covered by small patches like a jigsaw puzzle. We introduce a filtering step to ensure that found images do not overlap themselves when warped on the input image. A typical new application is to retrieve which products are proposed on a supermarket shelf. This is achieved using only a large picture of the shelf and a database of all products available in the supermarket. Because the database can be large and the analysis should ideally be done in a few seconds, we compare the performances of two state of the art algorithms to search SIFT correspondences: Best-Bin-First algorithm on Kd-Tree and Locality Sensitive Hashing. We also introduce a modification in the LSH algorithm to adapt it to SIFT vectors.

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References

  1. 1.
    Beckmann, N., Kriegel, H.-P., Schneider, R., Seeger, B.: The r*-tree: an efficient and robust access method for points and rectangles. In: SIGMOD 1990: Proceedings of the 1990 ACM SIGMOD international conference on Management of data, pp. 322–331. ACM Press, New York (1990)CrossRefGoogle Scholar
  2. 2.
    Beis, J.S., Lowe, D.G.: Shape indexing using approximate nearest-neighbour search in high-dimensional spaces. In: CVPR 1997: Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR 1997), p. 1000. IEEE Computer Society Press, Washington, DC, USA (1997)Google Scholar
  3. 3.
    Bentley, J.L.: Multidimensional binary search trees used for associative searching. Commun. ACM 18(9), 509–517 (1975)MathSciNetMATHCrossRefGoogle Scholar
  4. 4.
    Böhm, C., Berchtold, S., Keim, D.A.: Searching in high-dimensional spaces: Index structures for improving the performance of multimedia databases. ACM Comput. Surv. 33(3), 322–373 (2001)CrossRefGoogle Scholar
  5. 5.
    de Vries, A.P., Mamoulis, N., Nes, N., Kersten, M.: Efficient k-nn search on vertically decomposed data. In: SIGMOD 2002: Proceedings of the 2002 ACM SIGMOD international conference on Management of data, pp. 322–333. ACM Press, New York (2002)CrossRefGoogle Scholar
  6. 6.
    Fischler, M.A., Bolles, R.C.: Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM 24(6), 381–395 (1981)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Foo, J.J., Sinha, R.: Pruning sift for scalable near-duplicate image matching. In: Bailey, J., Fekete, A. (eds.) Eighteenth Australasian Database Conference (ADC 2007), Ballarat, Australia. CRPIT, vol. 63, pp. 63–71. ACS (2007)Google Scholar
  8. 8.
    Geusebroek, J.-M., Burghouts, G.J., Smeulders, A.W.M.: The Amsterdam library of object images. Int. J. Comput. Vision 61(1), 103–112 (2005)CrossRefGoogle Scholar
  9. 9.
    Gionis, A., Indyk, P., Motwani, R.: Similarity search in high dimensions via hashing. The VLDB Journal, 518–529 (1999)Google Scholar
  10. 10.
    Joly, A., Frélicot, C., Buisson, O.: Feature statistical retrieval applied to content-based copy identification. In: ICIP, pp. 681–684 (2004)Google Scholar
  11. 11.
    Katayama, N., Satoh, S.: The sr-tree: an index structure for high-dimensional nearest neighbor queries. In: SIGMOD 1997: Proceedings of the 1997 ACM SIGMOD international conference on Management of data, pp. 369–380. ACM Press, New York (1997)CrossRefGoogle Scholar
  12. 12.
    Ke, Y., Sukthankar, R.: Pca-sift: A more distinctive representation for local image descriptors. In: CVPR (2), pp. 506–513 (2004)Google Scholar
  13. 13.
    Ke, Y., Sukthankar, R., Huston, L.: An efficient parts-based near-duplicate and sub-image retrieval system. In: MULTIMEDIA 2004: Proceedings of the 12th annual ACM international conference on Multimedia, pp. 869–876. ACM Press, New York (2004)CrossRefGoogle Scholar
  14. 14.
    Lawder, J.K., King, P.J.H.: Querying multi-dimensional data indexed using the hilbert space-filling curve. SIGMOD Record 30(1), 19–24 (2001)CrossRefGoogle Scholar
  15. 15.
    Liu, T., Moore, A.W., Gray, A.G., Yang, K.: An investigation of practical approximate nearest neighbor algorithms. In: NIPS (2004)Google Scholar
  16. 16.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 20, 91–110 (2004)CrossRefGoogle Scholar
  17. 17.
    Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 27(10), 1615–1630 (2005)CrossRefGoogle Scholar
  18. 18.
    Uhlmann, J.K.: Satisfying general proximity/similarity queries with metric trees. Inf. Process. Lett. 40(4), 175–179 (1991)MATHCrossRefGoogle Scholar
  19. 19.
    White, D.A., Jain, R.: Similarity indexing with the ss-tree. In: ICDE 1996: Proceedings of the Twelfth International Conference on Data Engineering, pp. 516–523. IEEE Computer Society, Los Alamitos (1996)CrossRefGoogle Scholar
  20. 20.
    Yang, Z., Ooi, W.T., Sun, Q.: Hierarchical, non-uniform locality sensitive hashing and its application to video identification. In: ICME, pp. 743–746 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Adrien Auclair
    • 1
  • Laurent D. Cohen
    • 2
  • Nicole Vincent
    • 1
  1. 1.CRIP5-SIPUniversity Paris-DescartesParisFrance
  2. 2.CEREMADEUniversity Paris-DauphinePARISFrance

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