Gamification as a Key Enabling Technology for Image Sensing and Content Tagging

Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 55)

Abstract

The advent of mobile phone with a multi-megapixel camera and autouploaders has democratised photography. Taking pictures and acquiring annotations is no longer an expensive task as it used to be. Yet performing these tasks in a systematically way is still very cumbersome for most users. In this paper, we outline two game mechanics that can be exploited for the purpose of large-scale image sensing and content annotation. Our first mechanic allows for better control over when, how and where people should acquire images. The problem with existent image providers is that their services usually do not cover the entire area of interest, are inaccurate or very expensive. Our second mechanism aims at making the annotation of crowd-sourcing images more engaging. It leverage on large end-user communities to annotate images while avoiding the pitfall of using annotations that are meaningful only to domain experts. Annotations that are not relevant to users’ interests cannot be directly leveraged to enable search and discovery. A drawback of using crowdsourced annotations is that they have low agreement rates. Our approach aims at a finding a balanced agreement rate between pre-established annotations and those defined by users.

References

  1. 1.
    Simões, B., De Amicis, R.: Digital earth in a user-centric perspective. In: 2014 Fifth International Conference on Computing for Geospatial Research and Application (COM. Geo), pp. 47–48. IEEE (2014)Google Scholar
  2. 2.
    Simões, B., Aksenov, P., Santos, P., Arentze, T., Amicis, R.D.: c-Space: fostering new creative paradigms based on recording and sharing “casual” videos through the internet. In: 2015 IEEE International Conference on Multimedia Expo Workshops (ICMEW), pp. 1–4, June 2015Google Scholar
  3. 3.
    Michael, D.R., Chen, S.L.: Serious Games: Games That Educate, Train, and Inform. Muska & Lipman/Premier-Trade (2005)Google Scholar
  4. 4.
    Djaouti, D., Alvarez, J., Jessel, J.-P., Rampnoux, O.: Origins of serious games. In: Serious Games and Edutainment Applications, pp. 25–43. Springer (2011)Google Scholar
  5. 5.
    Marczewski, A.: Gamification: a simple introduction. Andrzej Marczewski (2012)Google Scholar
  6. 6.
    Zichermann, G., Linder, J.: The gamification revolution (2013)Google Scholar
  7. 7.
    Ueyama, Y., Tamai, M., Arakawa, Y., Yasumoto, K.: Gamification-based incentive mechanism for participatory sensing. In: 2014 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops), pp. 98–103. IEEE (2014)Google Scholar
  8. 8.
    Von Ahn, L., Liu, R., Blum, M.: Peekaboom: a game for locating objects in images. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 55–64. ACM (2006)Google Scholar
  9. 9.
    Von Ahn, L., Dabbish, L.: Labeling images with a computer game. In: Proceedings of the SIGCHI Conference on Human factors in Computing Systems, pp. 319–326. ACM (2004)Google Scholar
  10. 10.
    Von Ahn, L., Ginosar, S., Kedia, M., Blum, M.: Improving image search with phetch. In: IEEE International Conference on Acoustics, Speech and Signal Processing, 2007, ICASSP 2007, vol. 4, pp. IV–1209. IEEE (2007)Google Scholar
  11. 11.
    Eickhoff, C., Harris, C.G., de Vries, A.P., Srinivasan, P.: Quality through flow and immersion: gamifying crowdsourced relevance assessments. In: Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 871–880. ACM (2012)Google Scholar
  12. 12.
    Cooper, S., Khatib, F., Treuille, A., Barbero, J., Lee, J., Beenen, M., Leaver-Fay, A., Baker, D., Popović, Z., et al.: Predicting protein structures with a multiplayer online game. Nature 466(7307), 756–760 (2010)CrossRefGoogle Scholar
  13. 13.
    de Croon, G., Gerke, P.K., Sprinkhuizen-Kuyper, I.: Crowdsourcing as a methodology to obtain large and varied robotic data sets. In: 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2014), pp. 1595–1600. IEEE (2014)Google Scholar
  14. 14.
    Tuite, K., Snavely, N., Hsiao, D.-Y., Tabing, N., Popovic, Z.: Photocity: training experts at large-scale image acquisition through a competitive game. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1383–1392, ser. CHI ’11. ACM, New York, NY, USA. http://doi.acm.org/10.1145/1978942.1979146 (2011)
  15. 15.
    Bell, M., Reeves, S., Brown, B., Sherwood, S., MacMillan, D., Ferguson, J., Chalmers, M.: Eyespy: supporting navigation through play. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 123–132. ACM (2009)Google Scholar
  16. 16.
    Bartle, R.: Hearts, clubs, diamonds, spades: players who suit muds. J. MUD Res. 1(1), 19 (1996)Google Scholar
  17. 17.
    Stewart, B.: Personality and play styles: a unified model. Gamasutra. http://www.gamasutra.com/view/feature/6474/personality_and_play_styles_a_.php (2011)

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.GraphitechTrentoItaly

Personalised recommendations