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Finnish Environment Institute | Suomen ympäristökeskus | Finlands miljöcentral

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Advanced machine learning methods for biomonitoring (AMBI)

Project description

I will develop machine learning algorithms to conquer challenges typically encountered automated image-based identification in biomonitoring. The considered challenges are i) unbalanced occurrence of different taxa, while most interesting taxa are rare, ii) variations in imaging conditions, such as lighting, which may harm identification accuracy, iii) detection of rare or invasive taxa that are absent in previously collected datasets, and iv) hierarchical nature of the identification task. I will apply state-of-the-art machine learning techniques and propose improvements to them to tackle the challenges.

Publications related to the project

Publications on taxa identification:

J. Raitoharju and K. Meissner, "On Confidences and Their Use in (Semi-)Automatic Multi-Image Taxa Identification," IEEE Symposium Series on Computational Intelligence (SSCI), 2019. IEEEXplore

F. Sohrab and J. Raitoharju, "Boosting Rare Benthic Macroinvertebrates Taxa Identification With One-Class Classification," IEEE Symposium Series on Computational Intelligence (SSCI), 2020. IEEEXplore, arXiv

J. Ärje, C. Melvad, M.R. Jeppesen, S.A. Madsen, J. Raitoharju, M.S. Rasmussen, A. Iosifidis, V. Tirronen, M. Gabbouj, K. Meissner, and T.T. Høye,  "Automatic image-based identification and biomass estimation of invertebrates," Methods in Ecology and Evolution, vol. 11, no. 8, 2020. BES

J. Ärje, J. Raitoharju, A. Iosifidis, V. Tirronen, K. Meissner, M. Gabbouj, S. Kiranyaz, and S. Kärkkäinen, "Human experts vs. machines in taxa recognition," Signal Processing: Image Communication, vol. 87, 2020. ScienceDirect, arXiv

T.T. Høye, J. Ärje, K. Bjerge, O.L.P. Hansen, A. Iosifidis, F. Leese, H.M.R. Mann, K. Meissner, C. Melvad, and J. Raitoharju, "Deep learning and computer vision will transform entomology," Proceedings of the National Academy of Sciences, 2021. PNAS, bioRxiv

Publications on outlier detection:

F. Sohrab, J. Raitoharju, A. Iosifidis and M. Gabbouj, "Ellipsoidal Subspace Support Vector Data Description," IEEE Access, vol. 8, pp. 122013-122025, 2020. IEEEXplore, arXiv

F. Sohrab, J. Raitoharju, A. Iosifidis, and M. Gabbouj, "Multimodal subspace support vector data description," Pattern Recognition, vol. 110, 2021. ScienceDirect, arXiv

Publications on color constancy:

F. Laakom, J. Raitoharju, A. Iosifidis, J. Nikkanen and M. Gabbouj, "Color Constancy Convolutional Autoencoder," IEEE Symposium Series on Computational Intelligence (SSCI), 2019. IEEEXplore, arXiv

F. Laakom, J. Raitoharju, A. Iosifidis, U. Tuna, J. Nikkanen and M. Gabbouj, "Probabilistic Color Constancy,"  IEEE International Conference on Image Processing (ICIP), 2020. IEEEXplore, arXiv

F. Laakom, N. Passalis,  J. Raitoharju, J. Nikkanen, A. Tefas, A. Iosifidis and M. Gabbouj., "Bag of Color Features for Color Constancy," IEEE Transactions on Image Processing, vol. 29, 2020. IEEEXplore, arXiv

F. Laakom, J. Raitoharju, J. Nikkanen, A. Iosifidis and M. Gabbouj, "INTEL-TAU: A Color Constancy Dataset," IEEE Access, vol. 9, pp. 39560-39567, 2021. doi. IEEEXplore

Publications on hierarchical networks:

N. Passalis, J. Raitoharju, A. Tefas and M. Gabbouj, "Adaptive Inference Using Hierarchical Convolutional Bag-of-Features for Low-Power Embedded Platforms," IEEE International Conference on Image Processing (ICIP), 2019. IEEEXplore

N. Passalis, J. Raitoharju, A. Tefas, and M. Gabbouj, "Efficient adaptive inference for deep convolutional neural networks using hierarchical early exits," Pattern Recognition, vol. 105, 2020. ScienceDirect

N. Passalis, J. Raitoharju, M. Gabbouj and A. Tefas, "Efficient Adaptive Inference Leveraging Bag-of-Features-based Early Exits," IEEE International Workshop on Multimedia Signal Processing (MMSP), 2020. IEEEXplore

More information

Senior Research Scientist Jenni Raitoharju

Published 2019-10-15 at 16:54, updated 2021-03-22 at 15:03