Open-Set Plankton Recognition

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F26%3A0200639" target="_blank" >RIV/00216305:26230/26:0200639 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1007/978-3-031-91672-4_11" target="_blank" >http://dx.doi.org/10.1007/978-3-031-91672-4_11</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-91672-4_11" target="_blank" >10.1007/978-3-031-91672-4_11</a>

Result language

angličtina

Original language name

Open-Set Plankton Recognition

Original language description

This paper considers open-set recognition (OSR) of plankton images. Plankton include a diverse range of microscopic aquatic organisms that have an important role in marine ecosystems as primary producers and as a base of food webs. Given their sensitivity to environmental changes, fluctuations in plankton populations offer valuable information about oceans' health and climate change motivating their monitoring. Modern automatic plankton imaging devices enable the collection of large-scale plankton image datasets, facilitating species-level analysis. Plankton species recognition can be seen as an image classification task and is typically solved using deep learning-based image recognition models. However, data collection in real aquatic environments results in imaging devices capturing a variety of non-plankton particles and plankton species not present in the training set. This creates a challenging fine-grained OSR problem, characterized by subtle differences between taxonomically close plankton species. We address this challenge by conducting extensive experiments on three OSR approaches using both phyto- and zooplankton images analyzing also on the effect of the rejection thresholds for OSR. The results demonstrate that high OSR accuracy can be obtained promoting the use of these methods in operational plankton research. We have made the data publicly available to the research community.

Czech name

—

Czech description

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Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Lecture Notes in Computer Science

ISBN

978-3-031-91671-7

ISSN

—

e-ISSN

1611-3349

Number of pages

17

Pages from-to

168-184

Publisher name

Springer Nature

Place of publication

Milan, Italy

Event location

Milan, Italy

Event date

Sep 29, 2024

Type of event by nationality

EUR - Evropská akce

UT code for WoS article

001544992100011