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Prototype-Based Classification for Image Analysis and Its Application to Crop Disease Diagnosis

  • Ernest Mwebaze
  • Michael Biehl
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 428)

Abstract

In this paper, provide an application of Learning Vector Quantization (LVQ)-based techniques for solving a real-world problem. We apply LVQ for automated diagnosis of crop disease in cassava plants using features extracted from images of plants’ leaves. The problem reduces to a five class problem in which we attempt to distinguish between a leaf from a health plant and leaves representing four different viral and bacterial diseases in cassava. We discuss the problem under additional constraints that the solution must easily be deployable on a mobile device with limited processing power. In this study we explore the right configuration of type of algorithm and type of features extracted from the leaves that optimally solves the problem. We apply different variations of LVQ and compare them with standard classification techniques (Naïve Bayes, SVM and KNN). Results point to a preference of color feature representations and LVQ-based algorithms.

Keywords

Prototype-based classification LVQ GLVQ GMLVQ DLVQ Multi-class classification Feature extraction Image analysis 

Notes

Acknowledgments

The authors would like to thank the Dr. Titus Alicai and Dr. Chris Omongo of the Uganda National Crop Resources Research Institute (NaCRRI), for granting us permission to access disease and pest surveillance data and for supporting the annotation of the data. This work is carried out with support from the Bill and Melinda Gates Foundation under the PEARL 1: Automated survey technology and spatial modeling of viral crop disease in cassava project.

References

  1. 1.
    Neural Networks Research Centre, Bibliography on the self-organizing map (SOM), learning vector quantization (LVQ): University of Technology, Helsinki (2002). http://liinwww.ira.uka.de/bibliography/Neural/SOM.LVQ.html
  2. 2.
    Aduwo, J.R., Mwebaze, E., Quinn, J.A.: Automated vision-based diagnosis of cassava mosaic disease. In: Perner, P. (ed.) Industrial Conference on Data Mining—Workshops, pp. 114–122. IBaI Publishing (2010)Google Scholar
  3. 3.
    Mwebaze, E., Schneider, P., Schleif, F.-M., Aduwo, J.R., Quinn, J.A., Haase, S., Villmann, T., Biehl, M.: Divergence based classification in learning vector quantization. Neural Comput. 74(9), 1429–1435 (2011)Google Scholar
  4. 4.
    Sato, A.S., Yamada, K.: Generalized learning vector quantization. In: Mozer, M.C., Touretzky, D.S., Hasselmo, M.E. (eds.) NIPS, vol. 8, pp. 423–429. MIT Press, Cambridge (1996)Google Scholar
  5. 5.
    Schneider, Petra, Biehl, Michael, Hammer, Barbara: Adaptive relevance matrices in learning vector quantization. Neural Comput. 21(12), 3532–3561 (2009)zbMATHMathSciNetCrossRefGoogle Scholar
  6. 6.
    Khosla, A., Xiao, J., Torralba, A., Oliva, A.: Memorability of image regions. In: Pereira, F., Burges, C.J.C., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems 25, pp. 296–304. Curran Associates Inc (2012)Google Scholar
  7. 7.
    van de Weijer, J., Schmid, C., Verbeek, J.: Learning color names from real-world images. In: IEEE Conference on Computer Vision and Pattern Recognition, 2007. CVPR ’07, pp. 1–8 (2007)Google Scholar
  8. 8.
    Khan, R., van de Weijer, J., Shahbaz Khan, F., Muselet, D., Ducottet, C., Barat, C.: Discriminative color descriptors. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2866–2873 (2013)Google Scholar
  9. 9.
    Wang, J., Yang, J., Yu, K., Lv, F., Huang, T., Gong, Y.: Locality-constrained linear coding for image classification. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3360–3367 (2010)Google Scholar
  10. 10.
    Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 2169–2178 (2006)Google Scholar
  11. 11.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)CrossRefGoogle Scholar
  12. 12.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005. CVPR 2005, vol. 1, pp. 886–893 (2005)Google Scholar
  13. 13.
    Papari, G., Bunte, K., Biehl, M.: Waypoint averaging and step size control in learning by gradient descent (technical report), volume MLR-2011-06 of Machine Learning Reports, pp. 16–26. University of Bielefeld (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.School of Computing & Informatics TechnologyMakerere UniversityKampalaUganda
  2. 2.Johann Bernoulli Institute for Mathematics and Computer ScienceUniversity of GroningenGroningenThe Netherlands

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