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Weakly Supervised Minirhizotron Image Segmentation with MIL-CAM

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Computer Vision – ECCV 2020 Workshops (ECCV 2020)

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Abstract

We present a multiple instance learning class activation map (MIL-CAM) approach for pixel-level minirhizotron image segmentation given weak image-level labels. Minirhizotrons are used to image plant roots in situ. Minirhizotron imagery is often composed of soil containing a few long and thin root objects of small diameter. The roots prove to be challenging for existing semantic image segmentation methods to discriminate. In addition to learning from weak labels, our proposed MIL-CAM approach re-weights the root versus soil pixels during analysis for improved performance due to the heavy imbalance between soil and root pixels. The proposed approach outperforms other attention map and multiple instance learning methods for localization of root objects in minirhizotron imagery.

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References

  1. Ahn, J., Cho, S., Kwak, S.: Weakly supervised learning of instance segmentation with inter-pixel relations. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2209–2218 (2019)

    Google Scholar 

  2. Andrews, S., Tsochantaridis, I., Hofmann, T.: Support vector machines for multiple-instance learning. In: Advances in Neural Information Processing Systems, pp. 577–584 (2003)

    Google Scholar 

  3. Badrinarayanan, V., Kendall, A., Cipolla, R.: SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481–2495 (2017)

    Article  Google Scholar 

  4. Bates, G.: A device for the observation of root growth in the soil. Nature 139(3527), 966–967 (1937)

    Article  Google Scholar 

  5. Chattopadhay, A., Sarkar, A., Howlader, P., Balasubramanian, V.N.: Grad-CAM++: generalized gradient-based visual explanations for deep convolutional networks. In: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 839–847. IEEE (2018)

    Google Scholar 

  6. Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834–848 (2017)

    Article  Google Scholar 

  7. Durand, T., Mordan, T., Thome, N., Cord, M.: WILDCAT: weakly supervised learning of deep convnets for image classification, pointwise localization and segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 642–651 (2017)

    Google Scholar 

  8. Durand, T., Thome, N., Cord, M.: WELDON: weakly supervised learning of deep convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4743–4752 (2016)

    Google Scholar 

  9. Heidari, M., et al.: A new method for root detection in minirhizotron images: hypothesis testing based on entropy-based geometric level set decision. Int. J. Eng. 27(1), 91–100 (2014)

    Google Scholar 

  10. Huang, Z., Wang, X., Wang, J., Liu, W., Wang, J.: Weakly-supervised semantic segmentation network with deep seeded region growing. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7014–7023 (2018)

    Google Scholar 

  11. Johnson, M., Tingey, D., Phillips, D., Storm, M.: Advancing fine root research with minirhizotrons. Environ. Exp. Bot. 45(3), 263–289 (2001)

    Article  Google Scholar 

  12. Kolesnikov, A., Lampert, C.H.: Seed, expand and constrain: three principles for weakly-supervised image segmentation. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 695–711. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46493-0_42

    Chapter  Google Scholar 

  13. Krähenbühl, P., Koltun, V.: Efficient inference in fully connected CRFs with Gaussian edge potentials. In: Advances in Neural Information Processing Systems, pp. 109–117 (2011)

    Google Scholar 

  14. Lee, J., Kim, E., Lee, S., Lee, J., Yoon, S.: FickleNet: weakly and semi-supervised semantic image segmentation using stochastic inference. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5267–5276 (2019)

    Google Scholar 

  15. Leistner, C., Saffari, A., Bischof, H.: MIForests: multiple-instance learning with randomized trees. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6316, pp. 29–42. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15567-3_3

    Chapter  Google Scholar 

  16. Lin, G., Shen, C., Van Den Hengel, A., Reid, I.: Efficient piecewise training of deep structured models for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3194–3203 (2016)

    Google Scholar 

  17. Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)

    Google Scholar 

  18. Omeiza, D., Speakman, S., Cintas, C., Weldermariam, K.: Smooth grad-CAM++: an enhanced inference level visualization technique for deep convolutional neural network models. arXiv preprint arXiv:1908.01224 (2019)

  19. Oquab, M., Bottou, L., Laptev, I., Sivic, J.: Is object localization for free?-weakly-supervised learning with convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 685–694 (2015)

    Google Scholar 

  20. Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)

    Article  Google Scholar 

  21. Papandreou, G., Chen, L.C., Murphy, K.P., Yuille, A.L.: Weakly-and semi-supervised learning of a deep convolutional network for semantic image segmentation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1742–1750 (2015)

    Google Scholar 

  22. Pinheiro, P.O., Collobert, R.: From image-level to pixel-level labeling with convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1713–1721 (2015)

    Google Scholar 

  23. Rahmanzadeh, B.H., Shojaedini, S.: Novel automated method for minirhizotron image analysis: Root detection using curvelet transform. Int. J. Eng. 29, 337–346 (2016)

    Google Scholar 

  24. Rewald, B., Ephrath, J.E.: Minirhizotron techniques. In: Plant Roots: The Hidden Half, pp. 1–15 (2013)

    Google Scholar 

  25. Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28

    Chapter  Google Scholar 

  26. Roy, A., Todorovic, S.: Combining bottom-up, top-down, and smoothness cues for weakly supervised image segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3529–3538 (2017)

    Google Scholar 

  27. Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-CAM: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017)

    Google Scholar 

  28. Smilkov, D., Thorat, N., Kim, B., Viégas, F., Wattenberg, M.: SmoothGrad: removing noise by adding noise. arXiv preprint arXiv:1706.03825 (2017)

  29. Smith, A.G., Petersen, J., Selvan, R., Rasmussen, C.R.: Segmentation of roots in soil with U-net. Plant Methods 16(1), 1–15 (2020)

    Article  Google Scholar 

  30. Tang, M., Djelouah, A., Perazzi, F., Boykov, Y., Schroers, C.: Normalized cut loss for weakly-supervised cnn segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1818–1827 (2018)

    Google Scholar 

  31. Waddington, J.: Observation of plant roots in situ. Can. J. Bot. 49(10), 1850–1852 (1971)

    Article  Google Scholar 

  32. Wang, H., Du, M., Yang, F., Zhang, Z.: Score-CAM: improved visual explanations via score-weighted class activation mapping. arXiv preprint arXiv:1910.01279 (2019)

  33. Wang, T., et al.: SegRoot: a high throughput segmentation method for root image analysis. Comput. Electron. Agric. 162, 845–854 (2019)

    Article  Google Scholar 

  34. Wei, Y., Feng, J., Liang, X., Cheng, M.M., Zhao, Y., Yan, S.: Object region mining with adversarial erasing: a simple classification to semantic segmentation approach. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1568–1576 (2017)

    Google Scholar 

  35. Wei, Y., Xiao, H., Shi, H., Jie, Z., Feng, J., Huang, T.S.: Revisiting dilated convolution: a simple approach for weakly-and semi-supervised semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7268–7277 (2018)

    Google Scholar 

  36. Xu, W., et al.: Overcoming small minirhizotron datasets using transfer learning. Comput. Electronics in Agriculture 175 (2020). https://doi.org/10.1016/j.compag.2020.105466

  37. Yasrab, R., Atkinson, J.A., Wells, D.M., French, A.P., Pridmore, T.P., Pound, M.P.: Rootnav 2.0: deep learning for automatic navigation of complex plant root architectures. GigaScience 8(11), giz123 (2019)

    Article  Google Scholar 

  38. Yu, G., et al.: Root identification in minirhizotron imagery with multiple instance learning. Mach. Visi. Appl. 31 (2020). https://doi.org/10.1007/s00138-020-01088-z

  39. Zare, A., Jiao, C., Glenn, T.: Discriminative multiple instance hyperspectral target characterization. IEEE Trans. Pattern Anal. Mach. Intell. 40(10), 2342–2354 (2017)

    Article  Google Scholar 

  40. Zeng, G., Birchfield, S.T., Wells, C.E.: Detecting and measuring fine roots in minirhizotron images using matched filtering and local entropy thresholding. Mach. Vis. Appl. 17(4), 265–278 (2006)

    Article  Google Scholar 

  41. Zeng, G., Birchfield, S.T., Wells, C.E.: Rapid automated detection of roots in minirhizotron images. Mach. Vis. Appl. 21(3), 309–317 (2010)

    Article  Google Scholar 

  42. Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2921–2929 (2016)

    Google Scholar 

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Acknowledgement

This work was supported by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research award number DE-SC0014156 and by the Advanced Research Projects Agency - Energy award number DE-AR0000820.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Florida, 32611, Gainesville, FL, USA

    Guohao Yu, Alina Zare & Weihuang Xu

  2. Argonne National Laboratory, 60439, Lemont, IL, USA

    Roser Matamala

  3. Division of Plant Sciences, University of Missouri, 65211, Columbia, MO, USA

    Joel Reyes-Cabrera & Felix B. Fritschi

  4. Department of Integrative Biology, University of Texas at Austin, 78712, Austin, TX, USA

    Thomas E. Juenger

Authors
  1. Guohao Yu
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  2. Alina Zare
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  3. Weihuang Xu
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  4. Roser Matamala
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  5. Joel Reyes-Cabrera
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  7. Thomas E. Juenger
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Correspondence to Alina Zare .

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Editors and Affiliations

  1. University of Clermont Auvergne, Clermont Ferrand, France

    Adrien Bartoli

  2. Università degli Studi di Udine, Udine, Italy

    Andrea Fusiello

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Yu, G. et al. (2020). Weakly Supervised Minirhizotron Image Segmentation with MIL-CAM. In: Bartoli, A., Fusiello, A. (eds) Computer Vision – ECCV 2020 Workshops. ECCV 2020. Lecture Notes in Computer Science(), vol 12540. Springer, Cham. https://doi.org/10.1007/978-3-030-65414-6_30

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  • DOI: https://doi.org/10.1007/978-3-030-65414-6_30

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  • Online ISBN: 978-3-030-65414-6

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