Image Annotation in Presence of Noisy Labels

  • V. Chandrashekar
  • Shailesh Kumar
  • C. V. Jawahar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8251)


Labels associated with social images are valuable source of information for tasks of image annotation, understanding and retrieval. These labels are often found to be noisy, mainly due to the collaborative tagging activities of users. Existing methods on annotation have been developed and verified on noise free labels of images. In this paper, we propose a novel and generic framework that exploits the collective knowledge embedded in noisy label co-occurrence pairs to derive robust annotations. We compare our method with a well-known image annotation algorithm and show its superiority in terms of annotation accuracy on benchmark Corel5K and ESP datasets in presence of noisy labels.


Image Annotation Semantic concepts Graph Mining 


  1. 1.
    Makadia, A., Pavlovic, V., Kumar, S.: Baselines for image annotation. International Journal of Computer Vision 90(1), 88–105 (2010)CrossRefGoogle Scholar
  2. 2.
    Verma, Y., Jawahar, C.V.: Image annotation using metric learning in semantic neighbourhoods. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part III. LNCS, vol. 7574, pp. 836–849. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  3. 3.
    Weston, J., Bengio, S., Usunier, N.: Wsabie: Scaling up to large vocabulary image annotation. In: IJCAI (2011)Google Scholar
  4. 4.
    Guillaumin, M., Mensink, T., Verbeek, J., Schmid, C.: Tagprop: Discriminative metric learning in nearest neighbor models for image auto-annotation. In: ICCV, pp. 309–316 (2009)Google Scholar
  5. 5.
    Jin, Y., Khan, L., Wang, L., Awad, M.: Image annotations by combining multiple evidence and wordnet. ACM Multimedia (2005)Google Scholar
  6. 6.
    Wang, M., Zhou, X., Xu, H.: Web image annotation based on automatically obtained noisy training set. In: Zhang, Y., Yu, G., Bertino, E., Xu, G. (eds.) APWeb 2008. LNCS, vol. 4976, pp. 637–648. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  7. 7.
    Opsahl, T., Agneessens, F., Skvoretz, J.: Node centrality in weighted networks: Generalizing degree and shortest paths. Social Networks 32(3), 245–251 (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • V. Chandrashekar
    • 1
  • Shailesh Kumar
    • 2
  • C. V. Jawahar
    • 1
  1. 1.IIIT-HyderabadIndia
  2. 2.Google Inc.HyderabadIndia

Personalised recommendations