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.








Similar content being viewed by others
References
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
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)
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
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
Daniel MP, Denis M (1998) Spatial descriptions as navigational aids: a cognitive analysis of route directions. Kognitionswissenschaft 7(1):45–52
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
Duckham M, Winter S, Robinson M (2010) Including landmarks in routing instructions. J Locat Based Serv 4(1):28–52
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
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)
Google map. https://maps.google.com/
Grabler F, Agrawala M, Sumner RW, Pauly M (2008) Automatic generation of tourist maps. ACM Trans Graph 27:100,1–100,11
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
Kirk R (1982) Experimental design, 2nd edn. Brooks/Cole Publishing Company
Kopf J, Chen B, Szeliski R, Cohen M (2010) Street slide: browsing street level imagery. ACM Trans Graph 29(4):96,1–96,8
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
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
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
Lloyd S (2006) Least squares quantization in pcm. IEEE Trans Inf Theor 28 (2):129–137
Román A, Lensch HP (2006) Automatic multiperspective images. In: Proceedings of the 17th Eurographics conference on rendering techniques, EGSR’06, pp 83–92
Route 66 maps + navigation. https://play.google.com/store/apps/details?id=com.route66.maps5
Secord A, Lu J, Finkelstein A, Singh M, Nealen A (2011) Perceptual models of viewpoint preference. ACM Trans Graph (TOG) 30(5):109
Snavely N, Seitz SM, Szeliski R (2006) Photo tourism: exploring photo collections in 3d. ACM Trans Graph (TOG) 25(3):835–846
Vincent L (2007) Taking online maps down to street level. Computer 40(12):118–120
Wikipedia: Publicity, http://en.wikipedia.org/wiki/Publicity
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
Author information
Authors and Affiliations
Corresponding author
Electronic supplementary material
Below is the link to the electronic supplementary material.
(MP4 184 MB)
(MP4 19.0 MB)
Rights and permissions
About this article
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
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-018-5946-0