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Vote Aggregation Techniques in the Geo-Wiki Crowdsourcing Game: A Case Study

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 661)

Abstract

The Cropland Capture game (CCG) aims to map cultivated lands using around 170000 satellite images. The contribution of the paper is threefold: (a) we improve the quality of the CCG’s dataset, (b) we benchmark state-of-the-art algorithms designed for an aggregation of votes in a crowdsourcing-like setting and compare the results with machine learning algorithms, (c) we propose an explanation for surprisingly similar accuracy of all examined algorithms. To accomplish (a), we detect image duplicates using the perceptual hash function pHash. In addition, using a blur detection algorithm, we filter out unidentifiable images. In part (c), we suggest that if all workers are accurate, the task assignment in the dataset is highly irregular, then state-of-the-art algorithms perform on a par with Majority Voting. We increase the estimated consistency with expert opinions from 77% to 91% and up to 96% if we restrict our attention to images with more than 9 votes.

Keywords

Crowdsourcing Image processing Votes aggregation 

Notes

Acknowledgments

This research was supported by Russian Science Foundation, grant no. 14-11-00109, and the EU-FP7 funded ERC CrowdLand project, grant no. 617754.

References

  1. 1.
    Chatterjee, S., Bhattacharyya, M.: A biclustering approach for crowd judgment analysis. In: Proceedings of the Second ACM IKDD Conference on Data Sciences. pp. 118–119. ACM (2015)Google Scholar
  2. 2.
    Comber, A., Brunsdon, C., See, L., Fritz, S., McCallum, I.: Comparing expert and non-expert conceptualisations of the land: an analysis of crowdsourced land cover data. In: Tenbrink, T., Stell, J., Galton, A., Wood, Z. (eds.) COSIT 2013. LNCS, vol. 8116, pp. 243–260. Springer, Heidelberg (2013). doi: 10.1007/978-3-319-01790-7_14 CrossRefGoogle Scholar
  3. 3.
    Dawid, A.P., Skene, A.M.: Maximum likelihood estimation of observer error-rates using the EM algorithm. Appl. Stat. 28, 20–28 (1979)CrossRefGoogle Scholar
  4. 4.
    Dempster, A.P., et al.: Maximum likelihood from incomplete data via the EM algorithm. JRSS Ser. B 39, 1–38 (1977)MathSciNetzbMATHGoogle Scholar
  5. 5.
    Jagabathula, S., et al.: Reputation-based worker filtering in crowdsourcing. In: Advances in Neural Information Processing Systems, pp. 2492–2500 (2014)Google Scholar
  6. 6.
    Karger, D.R., Oh, S., Shah, D.: Iterative learning for reliable crowdsourcing systems. In: Advances in Neural Information Processing Systems, pp. 1953–1961 (2011)Google Scholar
  7. 7.
    Khattak, F.K., Salleb-Aouissi, A.: Improving crowd labeling through expert evaluation. In: 2012 AAAI Spring Symposium Series (2012)Google Scholar
  8. 8.
    Kim, H.C., Ghahramani, Z.: Bayesian classifier combination. In: International conference on Artificial Intelligence and Statistics, pp. 619–627 (2012)Google Scholar
  9. 9.
    Liu, Q., Peng, J., Ihler, A.T.: Variational inference for crowdsourcing. In: Advances in Neural Information Processing Systems, pp. 692–700 (2012)Google Scholar
  10. 10.
    Moreno, P.G., Teh, Y.W., Perez-Cruz, F., Artés-Rodríguez, A.: Bayesian nonparametric crowdsourcing. arXiv preprint arXiv:1407.5017 (2014)
  11. 11.
    Pareek, H., Ravikumar, P.: Human boosting. In: Proceedings of the 30th International Conference on Machine Learning (ICML2013), pp. 338–346 (2013)Google Scholar
  12. 12.
    Raykar, V.C.: Eliminating spammers and ranking annotators for crowdsourced labeling tasks. JMLR 13, 491–518 (2012)MathSciNetzbMATHGoogle Scholar
  13. 13.
    Raykar, V.C., et al.: Learning from crowds. J. Mach. Learn. Res. 11, 1297–1322 (2010)MathSciNetGoogle Scholar
  14. 14.
    Salk, C.F., Sturn, T., See, L., Fritz, S., Perger, C.: Assessing quality of volunteer crowdsourcing contributions: lessons from the cropland capture game. Int. J. Digit. Earth 9, 410–426 (2015)CrossRefGoogle Scholar
  15. 15.
    See, L., et al.: Building a hybrid land cover map with crowdsourcing and geographically weighted regression. ISPRS J. Photogramm. Remote Sens. 103, 48–56 (2015)CrossRefGoogle Scholar
  16. 16.
    Sheshadri, A., Lease, M.: Square: a benchmark for research on computing crowd consensus. In: First AAAI Conference on Human Computation and Crowdsourcing (2013)Google Scholar
  17. 17.
    Simpson, E., Roberts, S., Psorakis, I., Smith, A.: Dynamic Bayesian combination of multiple imperfect classifiers. In: Guy, T.V., Karny, M., Wolpert, D. (eds.) Decision Making and Imperfection, pp. 1–35. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  18. 18.
    Tong, H., Li, M., Zhang, H., Zhang, C.: Blur detection for digital images using wavelet transform. In: 2004 IEEE International Conference on Multimedia and Expo, ICME 2004, vol. 1, pp. 17–20. IEEE (2004)Google Scholar
  19. 19.
    Zauner, C.: Implementation and benchmarking of perceptual image hash functions. Ph.D. thesis (2010)Google Scholar
  20. 20.
    Zhu, X., et al.: Co-training as a human collaboration policy. In: AAAI (2011)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.International Institute for Applied Systems Analysis (IIASA)LaxenburgAustria
  2. 2.Krasovsky Institute of Mathematics and MechanicsEkaterinburgRussia
  3. 3.Ural Federal UniversityEkaterinburgRussia

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