A4 Article in conference proceedings
Improving Taxonomic Image-based Out-of-distribution Detection With DNA Barcodes (2024)


Impiö, M., & Raitoharju, J. (2024). Improving Taxonomic Image-based Out-of-distribution Detection With DNA Barcodes. In 32nd European Signal Processing Conference (EUSIPCO 2024) : Proceedings (pp. 1272-1276). IEEE. European Signal Processing Conference. https://doi.org/10.23919/eusipco63174.2024.10715139


JYU authors or editors


Publication details

All authors or editorsImpiö, Mikko; Raitoharju, Jenni

Parent publication32nd European Signal Processing Conference (EUSIPCO 2024) : Proceedings

Place and date of conferenceLyon, France26.-30.8.2024

ISBN979-8-3315-1977-3

eISBN978-9-4645-9361-7

Journal or seriesEuropean Signal Processing Conference

ISSN2219-5491

eISSN2076-1465

Publication year2024

Publication date26/08/2024

Pages range1272-1276

Number of pages in the book2761

PublisherIEEE

Publication countryUnited States

Publication languageEnglish

DOIhttps://doi.org/10.23919/eusipco63174.2024.10715139

Publication open accessNot open

Publication channel open access

Publication is parallel published (JYX)https://jyx.jyu.fi/handle/123456789/99083

Web address of parallel published publication (pre-print)https://arxiv.org/abs/2406.18999


Abstract

Image-based species identification could help scaling biodiversity monitoring to a global scale. Many challenges still need to be solved in order to implement these systems in real-world applications. A reliable image-based monitoring system must detect out-of-distribution (OOD) classes it has not been presented before. This is challenging especially with fine-grained classes. Emerging environmental monitoring techniques, DNA metabarcoding and eDNA, can help by providing information on OOD classes that are present in a sample. In this paper, we study if DNA barcodes can also support in finding the outlier images based on the outlier DNA sequence's similarity to the seen classes. We propose a re-ordering approach that can be easily applied on any pre-trained models and existing OOD detection methods. We experimentally show that the proposed approach improves taxonomic OOD detection compared to all common baselines. We also show that the method works thanks to a correlation between visual similarity and DNA barcode proximity. The code and data are available at https://github.com/lmikkoim/ldnaimg-ood.


Keywordsbiodiversitymonitoringdetermination of speciesclassificationsystematicscomputer visionDNA barcodessignal processingmachine learning

Free keywordsimage-based taxonomic identification; out-of-distribution detection; DNA barcodes


Contributing organizations


Ministry reportingYes

VIRTA submission year2024

Preliminary JUFO rating1


Last updated on 2025-16-01 at 20:06