Skip to main content

Advertisement

Log in

Destination selection based on consensus-selected landmarks

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

Abstract

This study aims at enhancing the destination look-up experience based on the fact that humans can easily recognize and remember images and icons of a destination instead of texts and numbers. Thus, this paper propose an algorithm to display buildings in hierarchical publicity and optimize the location distribution and orientation of each buildings. In the usual, the general navigation GPS include a lot of redundant information, and the necessary information always being drowned. Aimed to this point, we build the hierarchical structure according to their consensus-based publicity and spacial relationship to each other. The publicity is approximated by considering transportation importance and consensus visibility which reflects public consideration on metro transportation, opinions on popularity and famousness respectively. In addition to this, consensus-based optimal orientation of icon is optimized for easy recognition according to public preference estimated by clustering the view of public web photos. For the system evaluation, we perform four user studies to verify the effect of recognition and destination searching, and we all get positive response from these user studies.

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

Similar content being viewed by others

References

  1. Beeharee AK, Steed A (2006) A natural wayfinding exploiting photos in pedestrian navigation systems. In: Proceedings of the 8th conference on human-computer interaction with mobile devices and services, MobileHCI ’06, pp 81–88

  2. Bulbul A, Dahyot R (2015) Social media based 3d modeling and visualization. In: Proceedings of the 12th European conference on visual media production, CVMP ’15. ACM, New York, pp 20:1–20:1, https://doi.org/10.1145/2824840.2824860, (to appear in print)

  3. Chen W C, Battestini A, Gelfand N, Setlur V (2009) Visual summaries of popular landmarks from community photo collections. In: 2009 Conference record of the forty-third asilomar conference on signals, systems and computers. IEEE, pp 1248–1255

  4. Corsini M, Dellepiane M, Ganovelli F, Gherardi R, Fusiello A, Scopigno R (2013) Fully automatic registration of image sets on approximate geometry. Int J Comput Vis 102(1–3):91–111

    Article  Google Scholar 

  5. Daniel MP, Denis M (1998) Spatial descriptions as navigational aids: a cognitive analysis of route directions. Kognitionswissenschaft 7(1):45–52

    Article  Google Scholar 

  6. Deng H, Zhang L, Mao X, Qu H (2016) undefined, undefined, undefined, undefined: interactive urban context-aware visualization via multiple disocclusion operators. IEEE Trans Vis Comput Graph 22(7):1862–1874. https://doi.org/10.1109/TVCG.2015.2469661

    Article  Google Scholar 

  7. Duckham M, Winter S, Robinson M (2010) Including landmarks in routing instructions. J Locat Based Serv 4(1):28–52

    Article  Google Scholar 

  8. Gherardi R, Farenzena M, Fusiello A (2010) Improving the efficiency of hierarchical structure-and-motion. In: 2010 IEEE Conference on computer vision and pattern recognition (CVPR), pp 1594–1600

  9. Giannopoulos I, Kiefer P, Raubal M (2015) Gazenav: gaze-based pedestrian navigation. In: Proceedings of the 17th international conference on human-computer interaction with mobile devices and services, mobileHCI ’15. ACM, New York, pp 337–346, https://doi.org/10.1145/2785830.2785873, (to appear in print)

  10. Google map. https://maps.google.com/

  11. Grabler F, Agrawala M, Sumner RW, Pauly M (2008) Automatic generation of tourist maps. ACM Trans Graph 27:100,1–100,11

    Article  Google Scholar 

  12. Hile H, Grzeszczuk R, Liu A, Vedantham R, Košecka J, Borriello G (2009) Landmark-based pedestrian navigation with enhanced spatial reasoning. In: Proceedings of the 7th international conference on pervasive computing, pervasive ’09, pp 59–76

    Google Scholar 

  13. Kirk R (1982) Experimental design, 2nd edn. Brooks/Cole Publishing Company

  14. Kopf J, Chen B, Szeliski R, Cohen M (2010) Street slide: browsing street level imagery. ACM Trans Graph 29(4):96,1–96,8

    Article  Google Scholar 

  15. Ledda P, Chalmers A, Troscianko T, Seetzen H (2005) Evaluation of tone mapping operators using a high dynamic range display. In: ACM SIGGRAPH 2005, LA. ACM Press

  16. Li Y, Liu Y, Su Y, Hua G, Zheng N (2016) Three-dimensional traffic scenes simulation from road image sequences. IEEE Trans Intell Transp Syst 17(4):1121–1134. https://doi.org/10.1109/TITS.2015.2497408

    Article  Google Scholar 

  17. Liu F, Niu Y, Gleicher M (2009) Using web photos for measuring video frame interestingness. In: Proceedings of the 21st International jont conference on artifical intelligence, IJCAI’09, pp 2058–2063

  18. Lloyd S (2006) Least squares quantization in pcm. IEEE Trans Inf Theor 28 (2):129–137

    Article  MathSciNet  Google Scholar 

  19. Román A, Lensch HP (2006) Automatic multiperspective images. In: Proceedings of the 17th Eurographics conference on rendering techniques, EGSR’06, pp 83–92

  20. Route 66 maps + navigation. https://play.google.com/store/apps/details?id=com.route66.maps5

  21. Secord A, Lu J, Finkelstein A, Singh M, Nealen A (2011) Perceptual models of viewpoint preference. ACM Trans Graph (TOG) 30(5):109

    Article  Google Scholar 

  22. Snavely N, Seitz SM, Szeliski R (2006) Photo tourism: exploring photo collections in 3d. ACM Trans Graph (TOG) 25(3):835–846

    Article  Google Scholar 

  23. Vincent L (2007) Taking online maps down to street level. Computer 40(12):118–120

    Article  Google Scholar 

  24. Wikipedia: Publicity, http://en.wikipedia.org/wiki/Publicity

  25. Wither J, Au CE, Rischpater R, Grzeszczuk R (2013) Moving beyond the map: automated landmark based pedestrian guidance using street level panoramas. In: Proceedings of the 15th international conference on human-computer interaction with mobile devices and services, mobileHCI ’13, pp 203–212

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Pei-Ying Chiang.

Electronic supplementary material

Below is the link to the electronic supplementary material.

(MP4 184 MB)

(MP4 19.0 MB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chiang, PY., Hung, SH., Lai, YC. et al. Destination selection based on consensus-selected landmarks. Multimed Tools Appl 77, 30011–30033 (2018). https://doi.org/10.1007/s11042-018-5946-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-018-5946-0

Keywords

Navigation