On kNN Classification and Local Feature Based Similarity Functions

  • Giuseppe Amato
  • Fabrizio Falchi
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 271)


In this paper we consider the problem of image content recognition and we address it by using local features and kNN based classification strategies. Specifically, we define a number of image similarity functions relying on local features comparing their performance when used with a kNN classifier. Furthermore, we compare the whole image similarity approach with a novel two steps kNN based classification strategy that first assigns a label to each local feature in the document to be classified and then uses this information to assign a label to the whole image. We perform our experiments solving the task of recognizing landmarks in photos.


Image classification Recognition Landmarks Pattern recognition Machine learning Local features 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Amato, G., Falchi, F.: Local feature based image similarity functions for kNN classfication. In: Proceedings of the 3rd International Conference on Agents and Artificial Intelligence (ICAART 2011), vol. 1, pp. 157–166. SciTe Press (2011)Google Scholar
  2. 2.
    Amato, G., Falchi, F., Bolettieri, P.: Recognizing landmarks using automated classification techniques: an evaluation of various visual features. In: Proceeding of the Second Interantional Conference on Advances in Multimedia (MMEDIA 2010), Athens, Greece, June 13-19, pp. 78–83. IEEE Computer Society (2010)Google Scholar
  3. 3.
    Ballard, D.H.: Generalizing the hough transform to detect arbitrary shapes. Pattern Recognition 13(2), 111–122 (1981)zbMATHCrossRefGoogle Scholar
  4. 4.
    Batko, M., Novak, D., Falchi, F., Zezula, P.: Scalability comparison of peer-to-peer similarity search structures. Future Generation Comp. Syst. 24(8), 834–848 (2008)CrossRefGoogle Scholar
  5. 5.
    Bay, H., Tuytelaars, T., Van Gool, L.: SURF: Speeded Up Robust Features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006, Part I. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  6. 6.
    Boiman, O., Shechtman, E., Irani, M.: In defense of nearest-neighbor based image classification. In: CVPR (2008)Google Scholar
  7. 7.
    Chen, T., Wu, K., Yap, K.H., Li, Z., Tsai, F.S.: A survey on mobile landmark recognition for information retrieval. In: MDM 2009: Proc. of the Tenth International Conference on Mobile Data Management, pp. 625–630. IEEE (2009)Google Scholar
  8. 8.
    Dudani, S.: The distance-weighted k-nearest-neighbour rule. IEEE Transactions on Systems, Man and Cybernetics SMC-6(4), 325–327 (1975)CrossRefGoogle Scholar
  9. 9.
    Fagni, T., Falchi, F., Sebastiani, F.: Image classification via adaptive ensembles of descriptor-specific classifiers. Pattern Recognition and Image Analysis 20, 21–28 (2010), CrossRefGoogle Scholar
  10. 10.
    Falchi, F.: Pisa landmarks dataset (2011), (last accessed on March 3, 2011)
  11. 11.
    Google: Google Goggles (2011), (last accessed on March 3, 2011)
  12. 12.
    Jégou, H., Douze, M., Schmid, C.: Improving bag-of-features for large scale image search. Int. J. Comput. Vision 87(3), 316–336 (2010)CrossRefGoogle Scholar
  13. 13.
    Kennedy, L.S., Naaman, M.: Generating diverse and representative image search results for landmarks. In: WWW 2008: Proceeding of the 17th International Conference on World Wide Web, pp. 297–306. ACM Press, New York (2008)CrossRefGoogle Scholar
  14. 14.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  15. 15.
    Samet, H.: Foundations of Multidimensional and Metric Data Structures. Computer Graphics and Geometric Modeling. Morgan Kaufmann Publishers Inc., San Francisco (2005)Google Scholar
  16. 16.
    Serdyukov, P., Murdock, V., van Zwol, R.: Placing flickr photos on a map. In: Allan, J., Aslam, J.A., Sanderson, M., Zhai, C., Zobel, J. (eds.) SIGIR, pp. 484–491. ACM (2009)Google Scholar
  17. 17.
    Yeh, T., Tollmar, K., Darrell, T.: Searching the web with mobile images for location recognition. In: CVPR (2), pp. 76–81 (2004)Google Scholar
  18. 18.
    Zezula, P., Amato, G., Dohnal, V., Batko, M.: Similarity Search: The Metric Space Approach. In: Advances in Database Systems, vol. 32. Springer, Heidelberg (2006)Google Scholar
  19. 19.
    Zheng, Y., Song, M.Z., Adam, Y., Buddemeier, H., Bissacco, U., Brucher, A., Chua, F., Neven, T.S., Tour, H.: The world: Building a web-scale landmark recognition engine. In: CVPR, pp. 1085–1092. IEEE (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Giuseppe Amato
    • 1
  • Fabrizio Falchi
    • 1
  1. 1.ISTI-CNRPisaItaly

Personalised recommendations