Annotation-free learning of plankton for classification and anomaly detection

The acquisition of increasingly large plankton digital image datasets requires automatic methods of recognition and classification. As data size and collection speed increases, manual annotation and database representation are often bottlenecks for utilization of machine learning algorithms for taxo...

Full description

Saved in:
Bibliographic Details
Published in:Scientific reports 2020-07, Vol.10 (1), p.12142-12142, Article 12142
Main Authors: Pastore, Vito P., Zimmerman, Thomas G., Biswas, Sujoy K., Bianco, Simone
Format: Article
Language:English
Subjects:
631/114/1305
631/114/1386
704/829/826
Algorithms
Classification
Environmental monitoring
Humanities and Social Sciences
Learning algorithms
Machine learning
multidisciplinary
Plankton
Science
Science (multidisciplinary)
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The acquisition of increasingly large plankton digital image datasets requires automatic methods of recognition and classification. As data size and collection speed increases, manual annotation and database representation are often bottlenecks for utilization of machine learning algorithms for taxonomic classification of plankton species in field studies. In this paper we present a novel set of algorithms to perform accurate detection and classification of plankton species with minimal supervision. Our algorithms approach the performance of existing supervised machine learning algorithms when tested on a plankton dataset generated from a custom-built lensless digital device. Similar results are obtained on a larger image dataset obtained from the Woods Hole Oceanographic Institution. Additionally, we introduce a new algorithm to perform anomaly detection on unclassified samples. Here an anomaly is defined as a significant deviation from the established classification. Our algorithms are designed to provide a new way to monitor the environment with a class of rapid online intelligent detectors.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-020-68662-3