Partial Near-Duplicate Detection in Random Images by a Combination of Detectors

  • Andrzej Śluzek
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8192)


Detection of partial near-duplicates (e.g. similar objects) in random images continues to be a challenging problem. In particular, scalability of existing methods is limited because keypoint correspondences have to be confirmed by the configuration analysis for groups of matched keypoints. We propose a novel approach where pairs of images containing partial near-duplicates are retrieved if ANY number of keypoint matches is found between both images (keypoint descriptions are augmented by some geometric characteristics of keypoint neighborhoods). However, two keypoint detectors (Harris-Affine and Hessian-Affine) are independently applied, and only results confirmed by both detectors are eventually accepted. Additionally, relative locations of keypoint correspondences retrieved by both detectors are analyzed and (if needed) outlines of the partial near-duplicates can be extracted using a keypoint-based co-segmentation algorithm. Altogether, the approach has a very low complexity (i.e. it is scalable to large databases) and provides satisfactory performances. Most importantly, precision is very high, while recall (determined primarily by the selected keypoint description and matching approaches) remains at acceptable level.


keypoint description keypoint correspondences partial near-duplicates affine invariance object detection co-segmentation 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    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
  2. 2.
    Chum, O., Matas, J.: Large-scale discovery of spatially related images. IEEE PAMI 32(2), 371–377 (2010)CrossRefGoogle Scholar
  3. 3.
    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
  4. 4.
    Hochbaum, D., Singh, V.: An efficient algorithm for co-segmentation. In: Proc. ICCV 2009, Kyoto, pp. 269–276 (2009)Google Scholar
  5. 5.
    Jegou, H., Douze, M., Schmid, C.: Improving bag-of-features for large scale image search. International Journal of Computer Vision 87(3), 316–336 (2010)CrossRefGoogle Scholar
  6. 6.
    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
  7. 7.
    Mikolajczyk, K., Schmid, C.: Scale and affine invariant interest point detectors. International Journal of Computer Vision 60, 63–86 (2004)CrossRefGoogle Scholar
  8. 8.
    Mukherjee, L., Singh, V., Dyer, C.R.: Half-integrality based algorithms for cosegmentation of images. In: Proc. IEEE Conf. CVPR 2009, Miami Beach, pp. 2028–2035 (2009)Google Scholar
  9. 9.
    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
  10. 10.
    Romberg, S., August, M., Ries, C.X., Lienhart, R.: Robust feature bundling. In: Lin, W., Xu, D., Ho, A., Wu, J., He, Y., Cai, J., Kankanhalli, M., Sun, M.-T. (eds.) PCM 2012. LNCS, vol. 7674, pp. 45–56. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  11. 11.
    Sivic, J., Zisserman, A.: Efficient visual search of videos cast as text retrieval. IEEE PAMI 31(4), 591–606 (2009)CrossRefGoogle Scholar
  12. 12.
    Śluzek, A.: Large vocabularies for keypoint-based representation and matching of image patches. In: Fusiello, A., Murino, V., Cucchiara, R. (eds.) ECCV 2012 Ws/Demos, Part I. LNCS, vol. 7583, pp. 229–238. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  13. 13.
    Śluzek, A.: Inverted indexing in image fragment retrieval using huge keypoint-based vocabularies. In: Proc. CBMI 2013, Veszprem, pp. 167–172 (2013)Google Scholar
  14. 14.
    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., Zemčík, P. (eds.) ACIVS 2012. LNCS, vol. 7517, pp. 301–312. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  15. 15.
    Śluzek, A.: Zastosowanie metod momentowych do identyfikacji obiektów w cyfrowych systemach wizyjnych. WPW, Warszawa (1990)Google Scholar
  16. 16.
    Stewénius, H., Gunderson, S.H., Pilet, J.: Size matters: Exhaustive geometric verification for image retrieval. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part II. LNCS, vol. 7573, pp. 674–687. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  17. 17.
    Wu, Z., Ke, Q., Isard, M., Sun, J.: Bundling features for large scale partial-duplicate web image search. In: Proc. IEEE Conf. CVPR 2009, Miami Beach, pp. 25–32 (2009)Google Scholar
  18. 18.
    Yang, D., Śluzek, A.: Co-segmentation by keypoint matching: Incorporating pixel-to-pixel mapping into mrf. Tech. rep., Nanyang Technological University, SCE, Singapore (2010)Google Scholar
  19. 19.
    Zhao, W.-L., Ngo, C.-W.: Scale-rotation invariant pattern entropy for keypoint-based near-duplicate detection. IEEE Trans. on Image Processing 2, 412–423 (2009)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

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

Personalised recommendations