Skip to main content
Log in

Geotag propagation in social networks based on user trust model

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Notes

  1. http://www.flickr.com

  2. http://www.facebook.com

  3. http://images.google.com

  4. http://picasa.google.com

  5. http://www.panoramio.com

  6. http://www.zooomr.com

  7. http://www.wikitravel.com

  8. http://www.wikipedia.org

  9. http://www.epinions.com

References

  1. Ballard DH (1981) Generalizing the hough transform to detect arbitrary shapes. Pattern Recogn 13(2):111–122

    Article  MATH  Google Scholar 

  2. Brin S, Page L (1998) The anatomy of a large-scale hypertextual Web search engine. Comput Netw ISDN Syst 30(1–7):107–117

    Article  Google Scholar 

  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 121–130

  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 125–134

  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 169–172

  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 761–770

  7. Facebook Statistics. Available at: http://www.facebook.com/press/info.php?statistics

  8. Fellbaum C (ed) (1998) WordNet an electronic lexical database. MIT Press, Cambridge, London

    MATH  Google Scholar 

  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)

  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 1–8

  11. Hollenstein L, Purves R (2010) Exploring place through user-generated content: using Flickr to describe city cores. Journal of Spatial Information Science (JOSIS) 1:1–29

    Google Scholar 

  12. International Press Telecommunications Council (2009) IPTC photo metadata standard, IPTC Core 1.1 and IPTC Extension 1.1. Tech. rep.

  13. Jøsang A, Ismail R, Boyd C (2007) A survey of trust and reputation systems for online service provision. Decis Support Syst 43(2):618–644

    Article  Google Scholar 

  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 297–306

  15. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110

    Article  Google Scholar 

  16. Marti S, Garcia-Molina H (2006) Taxonomy of trust: categorizing P2P reputation systems. Comput Netw 50(4):472–484

    Article  MATH  Google Scholar 

  17. Massa P, Avesani P (2005) Controversial users demand local trust metrics: an experimental study on Epinions.com community. In: Proceedings of the international conference on artificial intelligence (IJCAI 2005), pp 121–126

  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 128–142

  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 2161–2168

  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 47–56

  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 categorization

  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 327–336

  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-3451

  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 254–257

  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 600–605

  26. Wikipedia—Flickr. Available at: http://en.wikipedia.org/wiki/Flickr

  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 361–370

  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 1085–1092

Download references

Acknowledgements

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).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ivan Ivanov.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Ivanov, I., Vajda, P., Lee, JS. et al. Geotag propagation in social networks based on user trust model. Multimed Tools Appl 56, 155–177 (2012). https://doi.org/10.1007/s11042-010-0570-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-010-0570-7

Keywords

Navigation