Large Scale Tag Recommendation Using Different Image Representations

  • Rabeeh Abbasi
  • Marcin Grzegorzek
  • Steffen Staab
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5887)

Abstract

Nowadays, geographical coordinates (geo-tags), social annotations (tags), and low-level features are available in large image datasets. In our paper, we exploit these three kinds of image descriptions to suggest possible annotations for new images uploaded to a social tagging system. In order to compare the benefits each of these description types brings to a tag recommender system on its own, we investigate them independently of each other. First, the existing data collection is clustered separately for the geographical coordinates, tags, and low-level features. Additionally, random clustering is performed in order to provide a baseline for experimental results. Once a new image has been uploaded to the system, it is assigned to one of the clusters using either its geographical or low-level representation. Finally, the most representative tags for the resulting cluster are suggested to the user for annotation of the new image. Large-scale experiments performed for more than 400,000 images compare the different image representation techniques in terms of precision and recall in tag recommendation.

References

  1. 1.
    Adrian, B., Sauermann, L., Roth-Berghofer, T.: Contag: A semantic tag recommendation system. In: Pellegrini, T., Schaffert, S. (eds.) Proceedings of I-Semantics 2007, September 2007, pp. 297–304. JUCS (2007)Google Scholar
  2. 2.
    Basile, P., Gendarmi, D., Lanubile, F., Semeraro, G.: Recommending smart tags in a social bookmarking system. In: Bridging the Gap between Semantic Web and Web 2.0 (SemNet 2007), pp. 22–29 (2007)Google Scholar
  3. 3.
    Bolettieri, P., Esuli, A., Falchi, F., Lucchese, C., Perego, R., Piccioli, T., Rabitti, F.: CoPhIR: a test collection for content-based image retrieval. CoRR, abs/0905.4627v2 (2009)Google Scholar
  4. 4.
    Cristani, M., Perina, A., Castellani, U., Murino, V.: Content visualization and management of geo-located image databases. In: CHI 2008: CHI 2008 extended abstracts on Human factors in computing systems, pp. 2823–2828. ACM, New York (2008)Google Scholar
  5. 5.
    Han, J., Kamber, M.: Data Mining: Concepts and Techniques, 2nd edn. Morgan Kaufmann, San Francisco (2006)Google Scholar
  6. 6.
    Heymann, P., Ramage, D., Garcia-Molina, H.: Social tag prediction. In: SIGIR 2008: Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval, pp. 531–538. ACM, New York (2008)CrossRefGoogle Scholar
  7. 7.
    Jäschke, R., Marinho, L., Hotho, A., Schmidt-Thieme, L., Stumme, G.: Tag recommendations in folksonomies. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds.) PKDD 2007. LNCS (LNAI), vol. 4702, pp. 506–514. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  8. 8.
    Kennedy, L., Naaman, M., Ahern, S., Nair, R., Rattenbury, T.: How flickr helps us make sense of the world: context and content in community-contributed media collections. In: MULTIMEDIA 2007: Proceedings of the 15th international conference on Multimedia, pp. 631–640. ACM, New York (2007)CrossRefGoogle Scholar
  9. 9.
    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, New York (2008)CrossRefGoogle Scholar
  10. 10.
    Mardia, K., Kent, J., Bibby, J.: Multivariate Analysis. Academic Press, London (1979)MATHGoogle Scholar
  11. 11.
    Moëllic, P.-A., Haugeard, J.-E., Pitel, G.: Image clustering based on a shared nearest neighbors approach for tagged collections. In: CIVR 2008: Proceedings of the 2008 international conference on Content-based image and video retrieval, pp. 269–278. ACM, New York (2008)CrossRefGoogle Scholar
  12. 12.
    Müller, H., Michoux, N., Bandon, D., Geissbuhler, A.: A review of content-based image retrieval systems in medical applications - clinical benefits and future directions. International Journal of Medical Informatics 73(1), 1–23 (2003)CrossRefGoogle Scholar
  13. 13.
    Pentland, A., Picard, R., Sclaroff, S.: Tools for content-based manipulation of image databases. International Journal of Computer Vision 18(3), 233–254 (1996)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Rabeeh Abbasi
    • 1
  • Marcin Grzegorzek
    • 1
  • Steffen Staab
    • 1
  1. 1.ISWeb - Information Systems and Semantic WebUniversity of Koblenz-LandauKoblenzGermany

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