A Supremum Norm Based Near Neighbor Search in High Dimensional Spaces

  • Nikolai Sergeev
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7594)


This paper presents a new near neighbor search. Feature vectors to be stored do not have to be of equal length. Two feature vectors are getting compared with respect to supremum norm. Time demand to learn a new feature vector does not depend on the number of vectors already learned. A query is formulated not as a single feature vector but as a set of features which overcomes the problem of possible permutation of components in a representation vector. Components of a learned feature vector can be cut out - the algorithm is still capable to recognize the remaining part.


Near neighbor search information retrieval 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [Berchtold et al., 2000]
    Berchtold, S., Keim, D.A., Kriegel, H.-P., Seidl, T.: A new technique for nearest neighbor search in. IEEE TKDE (2000)Google Scholar
  2. [Bishop, 2007]
    Bishop, C.: Neural Networks for Pattern Recognition. Oxford University Press (2007)Google Scholar
  3. [Caputo et al., 2000]
    Caputo, B., Hornegger, J., Paulus, D., Niemann, H.: A spin-glass markov random field for 3d object recognition. In: NIPS (2000)Google Scholar
  4. [Ciaccia et al., 1997]
    Ciaccia, P., Patella, M., Zezula, P.: M-tree: An efficient access. In: VLDB (1997)Google Scholar
  5. [Guttman, 2000]
    Guttman, A.: R-trees: A dynamic index structure for spatial. In: SIGMOD (2000)Google Scholar
  6. [Gyofri et al., 2002]
    Gyofri, L., Kohler, M., Krzyzak, A., Walk, H.: A Distribution-Free Theory of Nonparametric Regression. Springer (2002)Google Scholar
  7. [Nene et al., 1996]
    Nene, S.A., Nayar, S.K., Murase, H.: Columbia Object Image Library, COIL-100 (1996)Google Scholar
  8. [Obdrzalek and Matas, 2011]
    Obdrzalek, S., Matas, J.: Object recognition using local affine frames. In: BMVC (2011)Google Scholar
  9. [Pratt, 2001]
    Pratt, W.: Digital Image Processing. Wiley (2001)Google Scholar
  10. [Rosenblatt, 1962]
    Rosenblatt, F.: Principles of Neurodynamics. Spartan, New York (1962)Google Scholar
  11. [Sergeev and Palm, 2011]
    Sergeev, N., Palm, G.: A new object recognition system. In: VISAPP (2011)Google Scholar
  12. [Shakhnarovish et al., 2005]
    Shakhnarovish, Darrell, Indyk: Nearest-Neighbor Methods in Learning and Vision. MIT Press (2005)Google Scholar
  13. [Vapnik, 1998]
    Vapnik, V.N.: Statistical Learning Theory. Wiley, New York (1998)zbMATHGoogle Scholar
  14. [Yang et al., 2000]
    Yang, M.-H., Roth, D., Ahuja, N.: Learning to Recognize 3D Objects with SNoW. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1842, pp. 439–454. Springer, Heidelberg (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Nikolai Sergeev
    • 1
  1. 1.Institute for Neural Information ProcessingUniversity of UlmGermany

Personalised recommendations