Skip to main content
SpringerLink
Log in

Uncertainty-aware estimation of population abundance using machine learning

  • Special Issue Paper
  • Published:
Multimedia Systems Aims and scope Submit manuscript

Abstract

Machine learning is widely used for mining collections, such as images, sounds, or texts, by classifying their elements into categories. Automatic classification based on supervised learning requires groundtruth datasets for modeling the elements to classify, and for testing the quality of the classification. Because collecting groundtruth is tedious, a method for estimating the potential errors in large datasets based on limited groundtruth is needed. We propose a method that improves classification quality by using limited groundtruth data to extrapolate the potential errors in larger datasets. It significantly improves the counting of elements per class. We further propose visualization designs for understanding and evaluating the classification uncertainty. They support end-users in considering the impact of potential misclassifications for interpreting the classification output. This work was developed to address the needs of ecologists studying fish population abundance using computer vision, but generalizes to a larger range of applications. Our method is largely applicable for a variety of Machine learning technologies, and our visualizations further support their transfer to end-users.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

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

Similar content being viewed by others

Notes

  1. A measure of features’ similarity comparing an item to classify and a class model (Sect. 3.1).

References

  1. Alsallakh, B., Hanbury, A., Hauser, H., Miksch, S., Rauber, A.: Visual methods for analyzing probabilistic classification data. Vis. Comp. Graph. IEEE Trans. 20(12), 1703–1712 (2014)

  2. Beauxis-Aussalet, E., Hardman, L., van Ossenbruggen, J.: Deliverable D2.1 of the Fish4Knowledge Project-User information needs. Tech. rep. http://groups.inf.ed.ac.uk/f4k/DELIVERABLES/Del21.pdf

  3. Beauxis-Aussalet, E., Arslanova, E., Hardman, L., van Ossenbruggen, J.: A case study of trust issues in scientific video collections. In: Proceedings of the 2nd ACM international workshop on Multimedia analysis for ecological data. ACM (2013)

  4. Beauxis-Aussalet, E., Arslanova, E., Hardman, L., van Ossenbruggen, J.: A video processing and data retrieval framework for fish population monitoring. In: Proceedings of the 2nd ACM international workshop on Multimedia analysis for ecological data. ACM (2013)

  5. Beauxis-Aussalet, E., Hardman, L.: Visualization of confusion matrix for non-expert users. In: Poster at the IEEE Conference on Visualization—IEEE VIS (2014)

  6. Bliss, C.: The method of probits. Science 79(2037), 38–39 (1934)

    Article  Google Scholar 

  7. Chan, A.B., Liang, Z.S., Vasconcelos, N.: Privacy preserving crowd monitoring: counting people without people models or tracking. In: Conference on computer vision and pattern recognition—CVPR, pp. 1–7. IEEE (2008)

  8. Chen, C.: Top 10 unsolved information visualization problems. Computer Graphics and Applications, IEEE 25(4), 12–16 (2005)

    Article  Google Scholar 

  9. Correa, C.D., Chan, Y.H., Ma, K.L.: A framework for uncertainty-aware visual analytics. In: IEEE Symposium on visual analytics science and technology—VAST, pp. 51–58 (2009)

  10. Gibson, R., Barnes, M., Atkinson, R.: Practical measures of marine biodiversity based on relatedness of species. Oceanogr. Mar. Biol. Ann. Rev. 39, 207–231 (2001)

    Google Scholar 

  11. Hay, A.: The derivation of global estimates from a confusion matrix. Int. J. Remote Sens. 9(8), 1395–1398 (1988)

    Article  Google Scholar 

  12. Hetrick, N.J., Simms, K.M., Plumb, M.P., Larson, J.P.: Feasibility of using video technology to estimate salmon escapement in the Ongivinuk River, a clear-water tributary of the Togiak River. US Fish and Wildlife Service, King Salmon Fish and Wildlife Field Office (2004)

  13. Houben, S., Stallkamp, J., Salmen, J., Schlipsing, M., Igel, C.: Detection of traffic signs in real-world imagvucetices: the german traffic sign detection benchmark. In: International Joint Conference on Neural Networks. No. 1288 (2013)

  14. Huang, P.X., Boom, B.J., Fisher, R.B.: GMM improves the reject option in hierarchical classification for fish recognition. In: IEEE Winter Conference on applications of computer vision—WACV, pp. 371–376 (2014)

  15. Irvine, J., Ward, B., Teti, P., Cousens, N.: Evaluation of a method to count and measure live salmonids in the field with a video camera and computer. North Am. J. Fish. Manag. 11(1), 20–26 (1991)

    Article  Google Scholar 

  16. Johnson, C.: Top scientific visualization research problems. Comput. Graph. Appl. IEEE 24(4), 13–17 (2004)

    Article  Google Scholar 

  17. Jupp, D.L.B.: The stability of global estimates from confusion matrices. Int. J. Remote Sens. 10(9), 1563–1569 (1989)

    Article  Google Scholar 

  18. Lehmussola, A., Ruusuvuori, P., Selinummi, J., Huttunen, H., Yli-Harja, O.: Computational framework for simulating fluorescence microscope images with cell populations. Med. Imaging IEEE Trans 26(7), 1010–1016 (2007)

    Article  Google Scholar 

  19. Lempitsky, V.S., Zisserman, A.: Learning to count objects in images. In: NIPS. vol. 1, p. 2 (2010)

  20. Lip, C., Ramli, D.: Comparative study on feature, score and decision level fusion schemes for robust multibiometric systems. In: Sambath, S., Zhu, E. (eds.) Frontiers in computer education, advances in intelligent and soft computing, vol. 133, pp. 941–948. Springer, Berlin, Heidelberg (2012)

    Google Scholar 

  21. McCullagh, P., Nelder, J.A.: Generalized linear models, vol. 2. Chapman and Hall, London (1989)

  22. Platt, J.C.: Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In: Advances in large margin classifiers. Citeseer (1999)

  23. Saerens, M., Latinne, P., Decaestecker, C.: Adjusting the outputs of a classifier to new a priori probabilities: a simple procedure. Neural Comput. 14(1), 21–41 (2002)

    Article  MATH  Google Scholar 

  24. Tulp, I., Bolle, L.J., Rijnsdorp, A.D.: Signals from the shallows: in search of common patterns in long-term trends in dutch estuarine and coastal fish. J. Sea Res. 60(1), 54–73 (2008)

    Article  Google Scholar 

  25. Visser, H.: Estimation and detection of flexible trends. Atmos. Environ. 38(25), 4135–4145 (2004)

    Article  Google Scholar 

  26. Vucetic, S., Obradovic, Z.: Classification on data with biased class distribution. In: Proceedings of the 12th European conference on machine learning. pp. 527–538. Springer (2001)

  27. Ware, C.: Information visualization: perception for design. Elsevier (2013)

  28. Watson, D.L., Harvey, E.S., Anderson, M.J., Kendrick, G.A.: A comparison of temperate reef fish assemblages recorded by three underwater stereo-video techniques. Mar. Biol. 148(2), 415–425 (2005)

    Article  Google Scholar 

  29. Willis, T.J., Babcock, R.C.: A baited underwater video system for the determination of relative density of carnivorous reef fish. Mar. Freshw. Res. 51(8), 755–763 (2000)

    Article  Google Scholar 

  30. Yoshida, T., Akagi, K., Toda, T., Kushairi, M., Kee, A., Othman, B.: Evaluation of fish behaviour and aggregation by underwater videography in an artificial reef in tioman island, malaysia. Sains Malays. 39(3), 395–403 (2010)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Edinburgh, Edinburgh, UK

    Bastiaan J. Boom & Robert B. Fisher

  2. CWI, Amsterdam, The Netherlands

    Emma Beauxis-Aussalet & Lynda Hardman

Authors
  1. Bastiaan J. Boom
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Emma Beauxis-Aussalet
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lynda Hardman
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Robert B. Fisher
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Emma Beauxis-Aussalet.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Boom, B.J., Beauxis-Aussalet, E., Hardman, L. et al. Uncertainty-aware estimation of population abundance using machine learning. Multimedia Systems 22, 737–749 (2016). https://doi.org/10.1007/s00530-015-0479-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00530-015-0479-0

Keywords

Search

Navigation