A1 Journal article (refereed)
Human experts vs. machines in taxa recognition (2020)
Ärje, J., Raitoharju, J., Iosifidis, A., Tirronen, V., Meissner, K., Gabbouj, M., Kiranyaz, S., & Kärkkäinen, S. (2020). Human experts vs. machines in taxa recognition. Signal Processing : Image Communication, 87, Article 115917. https://doi.org/10.1016/j.image.2020.115917
JYU authors or editors
Publication details
All authors or editors: Ärje, Johanna; Raitoharju, Jenni; Iosifidis, Alexandros; Tirronen, Ville; Meissner, Kristian; Gabbouj, Moncef; Kiranyaz, Serkan; Kärkkäinen, Salme
Journal or series: Signal Processing : Image Communication
ISSN: 0923-5965
eISSN: 1879-2677
Publication year: 2020
Volume: 87
Article number: 115917
Publisher: Elsevier
Publication country: Netherlands
Publication language: English
DOI: https://doi.org/10.1016/j.image.2020.115917
Publication open access: Not open
Publication channel open access:
Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/70919
Publication is parallel published: https://arxiv.org/abs/1708.06899
Abstract
The step of expert taxa recognition currently slows down the response time of many bioassessments. Shifting to quicker and cheaper state-of-the-art machine learning approaches is still met with expert scepticism towards the ability and logic of machines. In our study, we investigate both the differences in accuracy and in the identification logic of taxonomic experts and machines. We propose a systematic approach utilizing deep Convolutional Neural Nets and extensively evaluate it over a multi-pose taxonomic dataset with hierarchical labels specifically created for this comparison. We also study the prediction accuracy on different ranks of taxonomic hierarchy in detail. We compare the results of Convolutional Neural Networks to human experts and support vector machines. Our results revealed that human experts using actual specimens yield the lowest classification error (CE¯=6.1%). However, a much faster, automated approach using deep Convolutional Neural Nets comes close to human accuracy (CE¯=11.4%) when a typical flat classification approach is used. Contrary to previous findings in the literature, we find that for machines following a typical flat classification approach commonly used in machine learning performs better than forcing machines to adopt a hierarchical, local per parent node approach used by human taxonomic experts (CE¯=13.8%). Finally, we publicly share our unique dataset to serve as a public benchmark dataset in this field.
Keywords: systematics; taxons; automated pattern recognition; machine learning; neural networks (information technology)
Free keywords: hierarchical classification; taxonomy; convolutional neural networks; taxonomic expert; multi-image data; biomonitoring
Contributing organizations
Related projects
- Eksaktisti approksimoidut Monte Carlo -m
- Vihola, Matti
- Research Council of Finland
- Advanced Computational and Statistical Techniques for Biomonitoring and Aquatic Ecosystem Service Management
- Kärkkäinen, Salme
- Research Council of Finland
Ministry reporting: Yes
Reporting Year: 2020
JUFO rating: 1