A Network-Centric Epidemic Approach for Automated Image Label Annotation

  • Mehdy Bohlool
  • Ronaldo Menezes
  • Eraldo Ribeiro
Part of the Communications in Computer and Information Science book series (CCIS, volume 116)


Automatically organizing and searching images by their content in large image datasets are major goals of Web-based search engines. Currently, these goals are accomplished by associating metadata information to each image in the database. In this paper, we investigate the use of network sciences to implement a metadata-propagation mechanism for images. We begin by creating a network of images connected by a part-based appearance-similarity measure, and propose an epidemiology-inspired model for metadata propagation. Our experiments show that organizing images as a network helps us label a large number images in the dataset in an economical way, i.e., with few manual metadata annotations.


Average Path Length Label Propagation Sift Descriptor Image Network Uniform Propagation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Amaral, L., Scala, A., Barthelemy, M., Stanley, H.: Classes of small-world networks. Proc. of the National Academy of Sciences of the United States of America 97(21), 11149 (2000)CrossRefGoogle Scholar
  2. 2.
    Barabási, A.-L., Albert, R.: Emergence of scaling in random networks. Science 286(5439), 509–512 (1999)MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Brin, S., Page, L.: The anatomy of a large-scale hypertextual web search engine. Computer Networks and ISDN Systems 30(1-7), 107–117 (1998)CrossRefGoogle Scholar
  4. 4.
    Cai, D., He, X., Li, Z., Ma, W., Wen, J.: Hierarchical clustering of www image search results using visual, textual and link information. In: Proceedings of the 12th Annual ACM International Conference on Multimedia, pp. 952–959. ACM, New York (2004)CrossRefGoogle Scholar
  5. 5.
    Chen, Q., Chang, H., Govindan, R., Jamin, S., Shenker, S., Willinger, W.: The origin of power-laws in internet topologies revisited. In: INFOCOM, vol. 2, pp. 608–617 (2002)Google Scholar
  6. 6.
    Granovetter, M.: The strength of weak ties: A network theory revisited. Sociological Theory 1, 201–233 (1983)CrossRefGoogle Scholar
  7. 7.
    Grauman, K., Darrell, T.: The pyramid match kernel: Efficient learning with sets of features. Journal of Machine Learning Research 8(2), 725–760 (2007)zbMATHGoogle Scholar
  8. 8.
    Hare, J., Lewis, P.: Saliency-based models of image content and their application to auto-annotation by semantic propagation. In: Proceedings of Multimedia and the Semantic Web/European Semantic Web Conference (2005)Google Scholar
  9. 9.
    Lowe, D.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  10. 10.
    Menezes, R., Wood, A.: The fading concept in tuple-space systems. In: ACM Symposium on Applied Computing, Dijon, France, pp. 440–444. ACM Press, New York (2006)Google Scholar
  11. 11.
    Pastor-Satorras, R., Vespignani, A.: Epidemic spreading in scale-free networks. Phys. Rev. Lett. 86(14), 3200–3203 (2001)CrossRefGoogle Scholar
  12. 12.
    Pastor-Satorras, R., Vespignani, A.: Epidemics and immunization in scale-free networks. In: Handbook of Graphs and Networks, pp. 111–130. Wiley Press, Chichester (2003)Google Scholar
  13. 13.
    Quack, T., Ferrari, V., Leibe, B., Van Gool, L., Zurich, E., Leuven, K., Zurich, S., Oxford, U., Leuven, B.: Efficient mining of frequent and distinctive feature configurations. In: Proc. of IEEE International Conference on Computer Vision. IEEE, Los Alamitos (2007)Google Scholar
  14. 14.
    Von Ahn, L., Dabbish, L.: Labeling images with a computer game. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 319–326. ACM, New York (2004)Google Scholar
  15. 15.
    Watts, D.J., Strogatz, S.H.: Collective dynamics of small world networks. Nature 393, 440–442 (1998)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Mehdy Bohlool
    • 1
  • Ronaldo Menezes
    • 1
  • Eraldo Ribeiro
    • 1
  1. 1.Florida Institute of TechnologyMelbourneUSA

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