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Large Vocabularies for Keypoint-Based Representation and Matching of Image Patches

  • Andrzej Śluzek
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7583)

Abstract

In large visual databases, detection of prospectively similar contents requires simple and robust methods. Keypoint correspondences are a popular approach which, nevertheless, cannot detect (using typical descriptions) similarities in a wider image context, e.g. detection of similar fragments. For such capabilities, the analysis of configuration constraints is needed. We propose keypoint descriptions which (by using sets of words from large vocabularies) represent semi-local characteristics of images. Thus, similar image patches (including similarly looking objects) can be preliminarily retrieved by straightforward keypoint matching. A limited-scale experimental verification is provided. The approach can be prospectively used as a simple mid-level feature matching in large and unpredictable visual databases.

Keywords

keypoint description keypoint correspondences visual vocabulary near-duplicate patches affine invariance 

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References

  1. 1.
    Fischler, M., Bolles, R.: Random Sample Consensus: a Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography. In: Buxton, B.F., Cipolla, R. (eds.) ECCV 1996. LNCS, vol. 1064, pp. 683–695. Springer, Heidelberg (1996)Google Scholar
  2. 2.
    Chum, O., Matas, J.: Matching with prosac - progressive sample consensus. In: Proc. IEEE Conf. CVPR 2005, San Diego, CA, pp. 220–226 (2005)Google Scholar
  3. 3.
    Wolfson, H., Rigoutsos, I.: Geometric hashing: An overview. IEEE Comp. Science and Engineering 4, 10–21 (1997)CrossRefGoogle Scholar
  4. 4.
    Chum, O., Perdoch, M., Matas, J.: Geometric min-hashing: Finding a (thick) needle in a haystack. In: Proc. IEEE Conf. CVPR 2009, pp. 17–24 (2009)Google Scholar
  5. 5.
    Lowe, D.G.: Object recognition from local scale-invariant features. In: Proc. 7th IEEE Int. Conf. Computer Vision, vol. 2, pp. 1150–1157 (1999)Google Scholar
  6. 6.
    Paradowski, M., Śluzek, A.: Local Keypoints and Global Affine Geometry: Triangles and Ellipses for Image Fragment Matching. In: Kwaśnicka, H., Jain, L.C. (eds.) Innovations in Intelligent Image Analysis. SCI, vol. 339, pp. 195–224. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  7. 7.
    Schmid, C., Mohr, R.: Object recognition using local characterization and semi-local constraints. Technical report, INRIA (1996)Google Scholar
  8. 8.
    Tell, D., Carlsson, S.: Combining Appearance and Topology for Wide Baseline Matching. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002, Part I. LNCS, vol. 2350, pp. 68–81. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  9. 9.
    Perd’och, M., Chum, O., Matas, J.: Efficient representation of local geometry for large scale object retrieval. In: Proc. IEEE Conf. CVPR 2009, pp. 9–16 (2009)Google Scholar
  10. 10.
    Jegou, H., Douze, M., Schmid, C.: Improving bag-of-features for large scale image search. International Journal of Computer Vision 87, 316–336 (2010)CrossRefGoogle Scholar
  11. 11.
    Śluzek, A., Paradowski, M.: Detection of Near-Duplicate Patches in Random Images Using Keypoint-Based Features. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P., Zemcik, P. (eds.) ACIVS 2011. LNCS, vol. 7517, pp. 301–312. Springer, Heidelberg (2012)Google Scholar
  12. 12.
    Mikolajczyk, K., Schmid, C.: Scale and affine invariant interest point detectors. International Journal of Computer Vision 60, 63–86 (2004)CrossRefGoogle Scholar
  13. 13.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60, 91–110 (2004)CrossRefGoogle Scholar
  14. 14.
    Schmid, C., Mohr, R.: Local grayvalue invariants for image retrieval. IEEE Trans. PAMI 19, 530–535 (1997)CrossRefGoogle Scholar
  15. 15.
    Yang, D., Śluzek, A.: A low-dimensional local descriptor incorporating tps warping for image matching. Image and Vision Computing 28, 1184–1195 (2010)CrossRefGoogle Scholar
  16. 16.
    Boureau, Y.L., Bach, F., LeCun, Y., Ponce, J.: Learning mid-level features for recognition. In: Proc. IEEE Conf. CVPR 2010, pp. 2559–2566 (2010)Google Scholar
  17. 17.
    Han, J.: Data mining for image/video processing: A promising research frontier. In: Proc. Int. Conf. on Content-based Image and Video Retrieval CIVR 2008, pp. 1–2 (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Andrzej Śluzek
    • 1
  1. 1.Khalifa UniversityAbu DhabiUAE

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