A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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-tekijät tai -toimittajat
Julkaisun tiedot
Julkaisun kaikki tekijät tai toimittajat: Ärje, Johanna; Raitoharju, Jenni; Iosifidis, Alexandros; Tirronen, Ville; Meissner, Kristian; Gabbouj, Moncef; Kiranyaz, Serkan; Kärkkäinen, Salme
Lehti tai sarja: Signal Processing : Image Communication
ISSN: 0923-5965
eISSN: 1879-2677
Julkaisuvuosi: 2020
Volyymi: 87
Artikkelinumero: 115917
Kustantaja: Elsevier
Julkaisumaa: Alankomaat
Julkaisun kieli: englanti
DOI: https://doi.org/10.1016/j.image.2020.115917
Julkaisun avoin saatavuus: Ei avoin
Julkaisukanavan avoin saatavuus:
Julkaisu on rinnakkaistallennettu (JYX): https://jyx.jyu.fi/handle/123456789/70919
Julkaisu on rinnakkaistallennettu: https://arxiv.org/abs/1708.06899
Tiivistelmä
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.
YSO-asiasanat: systematiikka (biologia); taksonit; hahmontunnistus (tietotekniikka); koneoppiminen; neuroverkot
Vapaat asiasanat: hierarchical classification; taxonomy; convolutional neural networks; taxonomic expert; multi-image data; biomonitoring
Liittyvät organisaatiot
Hankkeet, joissa julkaisu on tehty
- Eksaktisti approksimoidut Monte Carlo -m
- Vihola, Matti
- Suomen Akatemia
- Kehittyneiden laskenta- ja tilastomenetelmien soveltaminen biologiseen seurantaan ja ekosysteemipalvelujen hallintaan
- Kärkkäinen, Salme
- Suomen Akatemia
OKM-raportointi: Kyllä
Raportointivuosi: 2020
JUFO-taso: 1