Multimedia Tools and Applications

, Volume 56, Issue 1, pp 155–177 | Cite as

Geotag propagation in social networks based on user trust model

  • Ivan IvanovEmail author
  • Peter Vajda
  • Jong-Seok Lee
  • Lutz Goldmann
  • Touradj Ebrahimi


In the past few years sharing photos within social networks has become very popular. In order to make these huge collections easier to explore, images are usually tagged with representative keywords such as persons, events, objects, and locations. In order to speed up the time consuming tag annotation process, tags can be propagated based on the similarity between image content and context. In this paper, we present a system for efficient geotag propagation based on a combination of object duplicate detection and user trust modeling. The geotags are propagated by training a graph based object model for each of the landmarks on a small tagged image set and finding its duplicates within a large untagged image set. Based on the established correspondences between these two image sets and the reliability of the user, tags are propagated from the tagged to the untagged images. The user trust modeling reduces the risk of propagating wrong tags caused by spamming or faulty annotation. The effectiveness of the proposed method is demonstrated through a set of experiments on an image database containing various landmarks.


Tag propagation Social networks Object duplicate detection Geotags User trust model IPTC 



This work was supported by the Swiss National Foundation for Scientific Research in the framework of NCCR Interactive Multimodal Information Management (IM2), the Swiss National Science Foundation Grant “Multimedia Security” (number 200020-113709), and partially supported by the European Network of Excellence PetaMedia (FP7/2007-2011).


  1. 1.
    Ballard DH (1981) Generalizing the hough transform to detect arbitrary shapes. Pattern Recogn 13(2):111–122CrossRefzbMATHGoogle Scholar
  2. 2.
    Brin S, Page L (1998) The anatomy of a large-scale hypertextual Web search engine. Comput Netw ISDN Syst 30(1–7):107–117CrossRefGoogle Scholar
  3. 3.
    Cao L, Luo J, Huang T (2008) Annotating photo collections by label propagation according to multiple similarity cues. In: Proceeding of the 16th ACM international conference on multimedia (ACM MM 2008), pp 121–130Google Scholar
  4. 4.
    Cao L, Yu J, Luo J, Huang T (2009) Enhancing semantic and geographic annotation of web images via logistic canonical correlation regression. In: Proceedings of the 17th ACM international conference on multimedia (ACM MM 2009), pp 125–134Google Scholar
  5. 5.
    Carboni D, Sanna S, Zanarini P (2006) GeoPix: image retrieval on the geo web, from camera click to mouse click. In: Proceedings of the 8th ACM international conference on human-computer interaction with mobile devices and services (Mobile HCI 2006), pp 169–172Google Scholar
  6. 6.
    Crandall D, Backstrom L, Huttenlocher D, Kleinberg J (2009) Mapping the world’s photos. In: Proceedings of the 18th international conference on World Wide Web (WWW 2009), pp 761–770Google Scholar
  7. 7.
    Facebook Statistics. Available at:
  8. 8.
    Fellbaum C (ed) (1998) WordNet an electronic lexical database. MIT Press, Cambridge, LondonzbMATHGoogle Scholar
  9. 9.
    Gammeter S, Bossard L, Quack T, Van Gool L (2009) I know what you did last summer: object level auto-annotation of holiday snaps. In: Proceedings of the 20th international conference on computer vision (ICCV 2009)Google Scholar
  10. 10.
    Hays J, Efros AA (2008) im2gps: estimating geographic information from a single image. In: Proceedings of the IEEE international conference on computer vision and pattern recognition (CVPR 2008), pp 1–8Google Scholar
  11. 11.
    Hollenstein L, Purves R (2010) Exploring place through user-generated content: using Flickr to describe city cores. Journal of Spatial Information Science (JOSIS) 1:1–29Google Scholar
  12. 12.
    International Press Telecommunications Council (2009) IPTC photo metadata standard, IPTC Core 1.1 and IPTC Extension 1.1. Tech. rep.Google Scholar
  13. 13.
    Jøsang A, Ismail R, Boyd C (2007) A survey of trust and reputation systems for online service provision. Decis Support Syst 43(2):618–644CrossRefGoogle Scholar
  14. 14.
    Kennedy LS, Naaman M (2008) Generating diverse and representative image search results for landmarks. In: Proceedings of the 17th international conference on World Wide Web (WWW 2008), pp 297–306Google Scholar
  15. 15.
    Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110CrossRefGoogle Scholar
  16. 16.
    Marti S, Garcia-Molina H (2006) Taxonomy of trust: categorizing P2P reputation systems. Comput Netw 50(4):472–484CrossRefzbMATHGoogle Scholar
  17. 17.
    Massa P, Avesani P (2005) Controversial users demand local trust metrics: an experimental study on community. In: Proceedings of the international conference on artificial intelligence (IJCAI 2005), pp 121–126Google Scholar
  18. 18.
    Mikolajczyk K, Schmid C (2002) An affine invariant interest point detector. In: Proceedings of the 7th European conference on computer vision (ECCV 2002), pp 128–142Google Scholar
  19. 19.
    Nister D, Stewenius H (2006) Robust scalable recognition with a vocabulary tree. In: Proceedings of the IEEE international conference on computer vision and pattern recognition (CVPR 2006), pp 2161–2168Google Scholar
  20. 20.
    Quack T, Leibe B, Van Gool L (2008) World-scale mining of objects and events from community photo collections. In: Proceedings of the IEEE international conference on content-based image and video retrieval (CIVR 2008), pp 47–56Google Scholar
  21. 21.
    Sahami M, Dumais S, Heckerman D, Horvitz E (1998) A Bayesian approach to filtering junk e-mail. Tech. Rep. WS-98-05, AAAI-98 workshop on learning for text categorizationGoogle Scholar
  22. 22.
    Sigurbjörnsson B, van Zwol R (2008) Flickr tag recommendation based on collective knowledge. In: Proceeding of the 17th international conference on World Wide Web (WWW 2008), pp 327–336Google Scholar
  23. 23.
    Technical Standardization Committee on AV & IT Storage Systems and Equipment (2002) Exchangeable image file format for digital still cameras: exif Version 2.2. Tech. Rep. JEITA CP-3451Google Scholar
  24. 24.
    Vajda P, Dufaux F, Minh TH, Ebrahimi T (2009) Graph-based approach for 3D object duplicate detection. In: Proceedings of the international workshop on image analysis for multimedia interactive services (WIAMIS 2009), pp 254–257Google Scholar
  25. 25.
    Vajda P, Goldmann L, Ebrahimi T (2009) Analysis of the limits of graph-based object duplicate detection. In: Proceedings of the international symposium on multimedia, pp 600–605Google Scholar
  26. 26.
    Wikipedia—Flickr. Available at:
  27. 27.
    Wu L, Yang L, Yu N, Hua X (2009) Learning to tag. In: Proceedings of the 18th international conference on World Wide Web (WWW 2009), pp 361–370Google Scholar
  28. 28.
    Zheng Y, Zhao M, Song Y, Adam H, Buddemeier U, Bissacco A, Brucher F, Chua T, Neven H (2009) Tour the world: building a web-scale landmark recognition engine. In: Proceeding of the IEEE international conference on computer vision and pattern recognition (CVPR 2009), pp 1085–1092Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Ivan Ivanov
    • 1
    Email author
  • Peter Vajda
    • 1
  • Jong-Seok Lee
    • 1
  • Lutz Goldmann
    • 1
  • Touradj Ebrahimi
    • 1
  1. 1.Multimedia Signal Processing Group (MMSPG), Institute of Electrical Engineering (IEL)Ecole Polytechnique Fédérale de Lausanne (EPFL)LausanneSwitzerland

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